{"data":{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40740967", "kind": "science-record", "revision": 2, "content_hash": "sha256:5a5efa670c2fa1f20df8ff9fdb0ce425557b060c2f360ca155561319f8419203", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T14:01:45Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Midkine Expression as a Candidate Biomarker to Predict the Recurrence of Stage IA Lung Adenocarcinoma.", "doi": "10.1016/j.jtocrr.2025.100858", "pmid": "40740967", "authors": null, "journal": "JTO clinical and research reports", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 1.408, "unit": "hazard ratio", "tolerance": null, "source": "chars 947-1122 of source sha256 01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143"}, "evidence": [{"asset": "source:pmid:40740967", "locator": "chars 947-1122 of source sha256 01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143", "quote": "Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)"}]}], "gaps": ["abstract", "authors"]}, "experiment": {"state": "partial", "objective": "the headline numerical result the paper states for its main finding", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate and its interval from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The number a replication must land on, and the sentence it came from.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40740967"], "outputs": ["the published estimate, its unit and 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[], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40740967' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40740967", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.jtocrr.2025.100858", "sha256": "sha256:01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.jtocrr.2025.100858", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}, {"id": "asset:table:pmid:40740967:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:aee421d1afd61a06c4732516fc737bed8020bcaed631f55b3eef92440565ca1e", "bytes": 876, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[3]/table-wrap[1]#tbl1", "version": null, "note": "Clinical Characteristics of Patients", "structure": {"pmcid": "PMC12309940", "section_index": 3, "section_title": "Clinical Samples", "row_count": 25, "column_count": 2}, "table": {"label": "Table 1", "caption": "Clinical Characteristics of Patients", "rows": [["Characteristics, N = 62", "Total"], ["Sex", ""], ["Man", "28 (45.2%)"], ["Woman", "34 (54.8%)"], ["Age (y)", ""], ["Median [Range]", "58 [52–81]"], ["Smoking history", ""], ["Former/current", "21 (33.9%)"], ["Never", "41 (66.1%)"], ["Drinking history", ""], ["Former/current", "18 (29.0%)"], ["Never", "44 (71.0%)"], ["Clinical T stage", ""], ["1a", "7 (11.3%)"], ["1b", "37 (59.7%)"], ["1c", "18 (29.0%)"], ["EGFR mutation status", ""], ["EGFR mutated", "12 (19.4%)"], ["EGFR wild-type", "7 (11.3%)"], ["Unknown or not tested", "43 (69.4%)"], ["ALK rearrangement status", ""], ["ALK-rearranged", "1 (19.4%)"], ["ALK-negative", "14 (22.6%)"], ["Unknown or not tested", "47 (75.8%)"], ["Follow-up (mo), median [range]", "69 [7–112]"]], "row_count": 25, "column_count": 2, "numeric_cells": 21, "header_rows": 1, "locator": "section[3]/table-wrap[1]#tbl1"}}, {"id": "asset:table:pmid:40740967:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:2781703212a358d0f9c990e934609c00f8e586f739591b7d0a1015d41e8504a6", "bytes": 1573, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[9]/table-wrap[1]#tbl2", "version": null, "note": "Kruskal-Wallis Test for H Score Among Various Clinical Factors", "structure": {"pmcid": "PMC12309940", "section_index": 9, "section_title": "Expression of MDK Was Associated With Early Recurrence of Patients With Stage IA LUAD After Surgery", "row_count": 22, "column_count": 7}, "table": {"label": "Table 2", "caption": "Kruskal-Wallis Test for H Score Among Various Clinical Factors", "rows": [["Clinical Factors", "Intratumor H Score", "Peritumor H Score"], ["Median (range)", "χ2", "p Value", "Median (range)", "χ2", "p Value"], ["Sex", "", "", "", "", "", ""], ["Man", "141.2 (35.0–192.5)", "0.016", "0.899", "131.2 (105.0–165.0)", "0.711", "0.399"], ["Woman", "137.5 (62.5–200.0)", "", "", "130.0 (52.5–165.0)", "", ""], ["Age", "", "", "", "", "", ""], ["≤65", "140.0 (35.0–200.0)", "0.032", "0.858", "128.8 (52.5–165.0)", "6.520", "0.011"], [">65", "141.2 (110.0–192.5)", "", "", "138.8 (122.5–165.0)", "", ""], ["Smoking", "", "", "", "", "", ""], ["No", "135.0 (62.5–200.0)", "1.565", "0.211", "130.0 (52.5–165.0)", "0.983", "0.322"], ["Yes", "145.0 (35.0–192.5)", "", "", "132.5 (105.0–165.0)", "", ""], ["Drinking", "", "", "", "", "", ""], ["No", "141.2 (62.5–200.0)", "4.460", "0.035", "130.0 (52.5–165.0)", "0.176", "0.675"], ["Yes", "126.2 (35.0–185.0)", "", "", "130.0 (105.0–145.0)", "", ""], ["T stage", "", "", "", "", "", ""], ["1a", "135.0 (62.5–160.0)", "2.242", "0.326", "115.0 (107.5–130.0)", "8.731", "0.013"], ["1b", "140.0 (35.0–200.0)", "", "", "130.0 (52.5–165.0)", "", ""], ["1c", "145.0 (115.0–192.5)", "", "", "135.0 (105.0–165.0)", "", ""], ["EGFR", "", "", "", "", "", ""], ["Mutation", "152.5 (35.0–200.0)", "3.378", "0.185", "132.5 (115.0–150.0)", "1.860", "0.395"], ["Unknown", "135.0 (85.0–192.5)", "", "", "130.0 (52.5–165.0)", "", ""], ["WT", "150.0 (62.5–182.5)", "", "", "138.8 (107.5–155.0)", "", ""]], "row_count": 22, "column_count": 7, "numeric_cells": 59, "header_rows": 2, "locator": "section[9]/table-wrap[1]#tbl2"}}, {"id": "asset:table:pmid:40740967:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e02fdb30451e0519d0d035c0931b4fc7e936e069742486d281e58a0bedfe298d", "bytes": 971, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[1]#tbl3", "version": null, "note": "Univariate Cox Regression for RFS and OS", "structure": {"pmcid": "PMC12309940", "section_index": 10, "section_title": "Intratumoral MDK Expression Is a Strong Prognostic Factor for the Recurrence of Patients With Stage IA LUAD", "row_count": 9, "column_count": 5}, "table": {"label": "Table 3", "caption": "Univariate Cox Regression for RFS and OS", "rows": [["Clinical Factors", "RFS", "OS"], ["HR (95% CI)", "p Value", "HR (95% CI)", "p Value"], ["Age, y (>65 vs. ≤65)", "0.617 (0.215–1.768)", "0.368335692", "1.204 (0.250–5.797)", "0.817067823"], ["Sex (Male vs. Female)", "1.540 (0.751–3.157)", "0.238", "1.521 (0.408–5.665)", "0.531800817"], ["Smoking (Yes vs. No)", "2.968 (1.441–6.110)", "0.003", "2.537 (0.681–9.451)", "0.165274031"], ["Drinking (Yes vs. No)", "1.334 (0.624–2.853)", "0.457155442", "0.708 (0.147–3.409)", "0.666919433"], ["EGFR (Mutations vs. WT/Unknown)", "2.584 (1.201–5.558)", "0.015", "2.051 (0.513–8.207)", "0.30973669"], ["Intratumor H scorea", "1.408 (1.076–1.842)", "0.012607817", "1.888 (1.127–3.162)", "0.01576657"], ["Peritumor H scorea", "1.551 (0.946–2.543)", "0.081991066", "1.015 (0.465–2.217)", "0.969761457"]], "row_count": 9, "column_count": 5, "numeric_cells": 31, "header_rows": 2, "locator": "section[10]/table-wrap[1]#tbl3"}}, {"id": "asset:table:pmid:40740967:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:0dd4c5e35b03f3050186280d80f6b5f4713211da3ca88e9a1a96975516e53427", "bytes": 577, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[2]#tbl4", "version": null, "note": "Multivariate Cox Regression for RFS on the Basis of the Selected Independent Variables From the Stepwise Cox Regression", "structure": {"pmcid": "PMC12309940", "section_index": 10, "section_title": "Intratumoral MDK Expression Is a Strong Prognostic Factor for the Recurrence of Patients With Stage IA LUAD", "row_count": 4, "column_count": 6}, "table": {"label": "Table 4", "caption": "Multivariate Cox Regression for RFS on the Basis of the Selected Independent Variables From the Stepwise Cox Regression", "rows": [["Clinical Factors", "β", "SE", "z", "Wald p Value", "HR (95% CI)"], ["Smoking (Yes vs. No)", "1.080", "0.372", "2.899", "0.004", "2.944 (1.419–6.107)"], ["Intratumor H scorea", "0.014", "0.006", "2.261", "0.024", "1.316 (1.037–1.669)"], ["EGFR (Mutation vs. WT)", "0.878", "0.395", "2.224", "0.026", "2.407 (1.110–5.221)"]], "row_count": 4, "column_count": 6, "numeric_cells": 16, "header_rows": 1, "locator": "section[10]/table-wrap[2]#tbl4"}}], "gaps": ["asset:source:pmid:40740967 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40740967", "asset:table:pmid:40740967:1", "asset:table:pmid:40740967:2", "asset:table:pmid:40740967:3", "asset:table:pmid:40740967:4"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40740967"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "passed", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "61500460941474808b4f15ad7f637e5e6f4c55029199c983187ffca2f3444964", "source_hash": "01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143", "raw_response_sha256": "sha256:eff868f9e92e7c3cc89bbbc9824d90e2a5d105c0316f520fa66b3df1ce5af9c4", "raw_response_chars": 4432}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "72b5ed0702de4166fd4043b6e6d34ae3d2810b2e1b0579c5fe28770bf70a1e7e", "source_hash": "01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143", "raw_response_sha256": "sha256:ef37afbe17e8becddadb71b883c4972ef50df1e5b3c76454f529546feb3e4c0c", "raw_response_chars": 48099}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "16589132b9a032199998460544080d1f690fdfc370e0f7e339d8d55583c4fbb0", "source_hash": "01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143", "raw_response": "{\"parent_value\":1.408,\"unit\":\"hazard ratio\",\"evidence\":\"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)\",\"target_result\":\"Intratumoral MDK expression significantly increased the risk of recurrence in patients with stage IA lung adenocarcinoma.\",\"number_stated\":true}", "reading": {"parent_value": 1.408, "unit": "hazard ratio", "evidence": "Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)", "target_result": "Intratumoral MDK expression significantly increased the risk of recurrence in patients with stage IA lung adenocarcinoma.", "number_stated": true, "source_locator": "chars 947-1122 of source sha256 01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143"}, "recorded_at": "2026-10-31T15:26:39.919671+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "f54b948df5c5f6481bbaac1086aa26de695649696abfb9c096568a2f16c50003", "source_hash": "01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline main finding. Source abstract. Need infer registered target \"headline numerical result the paper states for its main finding.\" Likely hazard ratio 1.408 for intratumoral MDK and recurrence in univariate analysis? Or perhaps 62 patients, 30 early recurrence? Need parse wording. User asks \"find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target...\" Registered target generic main finding. Need identify headline numerical result. Paper title candidate biomarker predict recurrence. Abstract gives key numerical results multiple: intratumoral MDK significantly overexpressed p&lt;.001; HR 1.408 for recurrence; etc. \"headline number\" perhaps main finding is HR 1.408? Need choose likely. Need exact contiguous quote attaching target. Unit paper prints: hazard ratio? Unit could \"hazard ratio\" or perhaps no unit. parent_value number. Evidence exact clause. target_result perhaps \"Hazard ratio of 1.408 for increased risk of recurrence with intratumoral MDK expression.\" Need use whole clause exact. Which result is central?\n\nLet's understand likely benchmark task. Registered target generated perhaps \"the headline numerical result the paper states for its main finding.\" They expect one number from source. Often paper QA asks identify headline result. Source may be an abstract where key result first numerical effect size: \"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)...\" Main finding MDK predicts recurrence. The headline is likely HR 1.408. Could be cohort result: 62 patients, 30 recurrence. But \"main finding\" likely the effect estimate.\n\nNeed ensure exact evidence contiguous quotation contains number, and attribution by target. Quote whole clause: \"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013) and death (HR 1.888 [1.127–3.162], p = 0.016).\" This contains two headline numbers though parent only 1.408. User says report headline number only, evidence can quote whole clause and includes another. \"single numerical result\" maybe quote narrower exact span: \"intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)\" This is exact contiguous and contains p and CI, unavoidable. Could quote just \"hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013\" but target attachment? They explicitly say when attribution by list/respectively/words names target, quote whole clause so reader checks. Need include \"Univariate Cox regression analysis indicated that...\" maybe ends before \"and death\" but syntactic clause: \"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)\" exact contiguous, doesn't need include death. This is a clause attached target.\n\nCould headline numerical result instead be sample count 62? \"We collected surgical samples from 62 patients with stage IA LUAD, of which 30 patients had early recurrence and others without.\" Replication of main study has to reproduce cohort? But registered target explicitly \"headline numerical result the paper states for its main finding\", likely effect size. \"Headline number only\" often asks summary of paper's main numerical finding, perhaps in abstract. Let's investigate likely source and article maybe has key result. Could be Cancers paper. Abstract might originally include \"The optimal cut-off value of MDK expression was 100...\" omitted? Given supplied source text. Need only source.\n\nQuestion may stem from benchmark where target is deliberately broad and expected choose one main claim. Let's reason language: \"REGISTERED TARGET: the headline numerical result the paper states for its main finding\". This perhaps asks us to output a numerical result that replication must reproduce. Main finding = MDK candidate predictor of early recurrence. There are several numerical stats. Which is \"headline\"? Usually effect estimate directly tied to primary outcome: univariate Cox HR 1.408 for recurrence. The stepwise HR 1.316 also independent prognostic factor. Validation datasets. Primary cohort is 62. The first main effect result likely 1.408.\n\nCould they instead consider \"30 of 62 had early recurrence\" headline result? But no, not main finding, merely cohort. \"single numerical result\" perhaps sample size is not result. HR is result.\n\nNeed decide unit. Paper prints \"hazard ratio [HR]\". Unit string likely \"hazard ratio\" (dimensionless). Could use \"HR\" but asks unit the paper prints. It prints \"hazard ratio [HR]\" not a physical unit. So unit = \"hazard ratio\". parent_value = 1.408. evidence exact.\n\ntarget_result perhaps describe \"Intratumoral MDK expression significantly increased the risk of recurrence in stage IA lung adenocarcinoma.\" Should maybe mention cohort? They ask JSON. number_stated true.\n\nNeed ensure valid JSON, no Markdown. Evidence quotation exact contiguous. Could use full sentence? \"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013) and death (HR 1.888 [1.127–3.162], p = 0.016).\" They say do not shorten evidence; quote whole clause if attribution. We can quote exact contiguous substring ending before and death. Is that \"whole clause\"? Grammatically source has one clause coordinating predicates \"increased risk recurrence ... and death\". To check attribution of recurrence, enough. But phrase \"do not shorten it\" refers supplied exact quote? They instruct \"The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by ... When that is how the text links them, quote the whole clause...\" Means evidence quote must exact contiguous, likely can select relevant clause. Better use complete sentence, exact, though includes another result. \"Report headline number only\" parent_value only. Full sentence is safe attribution and no ellipsis. Evidence:\n\"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013) and death (HR 1.888 [1.127–3.162], p = 0.016).\"\nExact. But this could muddy single number. Still evidence contains target and other numerical result. User specifically asks exact contiguous quote contains number, not only number. They expect such context.\n\nAlternatively quote earlier:\n\"The intratumoral MDK was significantly overexpressed in patients with early recurrence (p &lt; 0.001).\" This is arguably headline finding and p-value, but likely no, effect size better.\n\nMaybe headline number is 1.316 because stepwise Cox independent prognostic factor for early recurrence, more headline than univariate. Let's deeply assess paper's intended \"main finding\". Title: \"Midkine Expression as a Candidate Biomarker to Predict the Recurrence of Stage IA Lung Adenocarcinoma\". Abstract:\n- 62 patients, 30 recurrence.\n- Intratumoral MDK overexpressed in early recurrence p&lt;0.001.\n- higher MDK poor RFS and OS.\n- Univariate HR 1.408 recurrence.\n- Stepwise independent smoking HR 2.944, MDK HR 1.316, EGFR HR 2.407.\n- TCGA HR1.559.\n- GSE31210 HR2.617.\n\nWhat is \"headline numerical result\" one replication target? Possibly the study's key primary comparison is intratumoral MDK expression in recurrence vs no recurrence, and abstract only gives p&lt;0.001, not a difference value. But if target main finding = recurrence risk, effect HR 1.408 (primary cohort univariable) or 1.316 (adjusted independent). Headline number might be 1.408 because recurrence endpoint specifically.\n\nThe registered target wording is generic, perhaps generated by user to test selection. Need return one.\n\nLet's inspect \"parent_value\" schema nomenclature. It might be from task requiring find number and unit for parent endpoint. \"parent_value\" likely numeric value. They phrase \"single numerical result that a replication of the registered target has to reproduce.\" Maybe registered target is an endpoint, and \"parent_value\" is target result. Here target text itself \"the headline numerical result the paper states for its main finding.\" No units. Could perhaps be sample size. Need map.\n\nCould be asking \"headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed.\" Often benchmark prompts ask extract numerical claim from article. The key output can be HR, p-value, sample count. \"registered target\" may be \"the headline numerical result\". We should state paper's principal estimate.\n\nLet's search mentally actual article maybe published in Cancers 2025? The abstract as supplied. Key conclusion MDK candidate. Main result likely \"intratumoral MDK expression significantly increased risk of recurrence (HR 1.408, 95% CI 1.076–1.842, p = 0.013).\" That is strongest direct central result. Use HR unit.\n\nCould target_result include exact interpretation and no additional number? E.g., \"The paper reports that intratumoral MDK expression significantly increased recurrence risk in stage IA lung adenocarcinoma.\" Good.\n\nEvidence perhaps only relevant sentence. Unit = \"hazard ratio\". Parent = 1.408.\n\nNeed obey developer \"extract claims only source\" yes.\n\nNo mention 95% CI because paper doesn't say 95%, so don't add. target_result can paraphrase? User says target_result says what text states if false; if true likely description. We can use source wording.\n\nShould evidence include \"Univariate Cox regression analysis...\" exact. Fine.\n\nLet's formulate JSON:\n{\n \"parent_value\":1.408,\n \"unit\":\"hazard ratio\",\n \"evidence\":\"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013) and death (HR 1.888 [1.127–3.162], p = 0.016).\",\n \"target_result\":\"Intratumoral MDK expression was associated with a significantly increased risk of recurrence in patients with stage IA LUAD.\",\n \"number_stated\":true\n}\nCould unit perhaps \"HR\" since paper prints abbreviation. They explicitly \"unit must be unit the paper prints.\" It prints \"hazard ratio [HR]\". Use \"hazard ratio\". Maybe unit expected \"HR\". Usually output unit field should be \"hazard ratio\" rather than blank. Could use \"HR\" but not physical. Better full.\n\nCould quote only:\n\"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)\"\nThis is exact contiguous substring and whole relevant clause, not shortened with ellipsis. Does omitting trailing coordinated phrase violate whole clause? Better include full sentence to unquestionably meet. User says \"do not shorten it\" perhaps complete source quote. Full sentence.\n\nWait, could they object evidence contains multiple numbers and \"headline number only\"? They mean answer number, evidence naturally has supporting context. Full sentence has another HR. Yet request \"Report the headline number only\" likely parent_value only and evidence quote as required. The clause ending after recurrence parenthetical is a contiguous quote, and no ellipsis. It is likely acceptable. \"quote the whole clause so a reader can check attribution\" means exact clause including attribution. The source's clause is whole sentence with \"and death\", but attribution is clear before and. A shorter contiguous substring still lets reader check. I'd select up to p=0.013 to avoid extraneous number. It is exact, contiguous, and semantically complete: \"Univariate ... recurrence (...)\" even though source continues. Is this a \"whole clause\"? Yes, arguably independent clause coordinated with another. No ellipsis. Good.\n\nDo we need include stage IA context in target_result? Evidence doesn't state stage IA in same sentence but prior source. Claim from source. Could target_result mention stage IA based title/background. Better keep directly source wording: \"Intratumoral MDK expression significantly increased the risk of recurrence.\" That's exact source concept.\n\nPotential issue: \"main finding\" maybe independent prognostic factor HR 1.316. Let's compare centrality. If replication of registered target = paper's main finding, perhaps replication must reproduce adjusted HR 1.316, since this demonstrates independent prognostic factor. Univariate 1.408 is total effect, while headline \"MDK as candidate biomarker\" based differential expression and survival. The abstract's key inferential claim likely \"Stepwise Cox regression ... intratumoral MDK expression (HR 1.316...) ... independent prognostic factors for early recurrence.\" If asked one headline number, which would a summary choose? Maybe 1.316 because it's independent risk factor. Yet \"headline numerical result the paper states for its main finding\" likely could refer to \"HR 1.408\" because the study's primary analysis. Need identify paper's stated result perhaps title/abstract. Let's inspect sequencing and wording:\n- \"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (HR 1.408...) and death...\"\n- \"Stepwise Cox regression analysis revealed that smoking (...), intratumoral MDK expression (HR 1.316...), and EGFR mutation (...) were independent prognostic factors for early recurrence.\"\nMain finding of MDK as candidate biomarker for recurrence might be adjusted HR 1.316.\nWhich \"headline number\" is more likely expected from benchmark? They might have a registry target with metric and value, perhaps \"headline numerical result\" is deliberately vague but source should have a prominent number in abstract. Could be 62. They might define target as \"headline numerical result paper states for main finding\" and ask to find one. Maybe expected p < .001? Let's explore task design.\n\nThe prompt wording:\n\"Read the supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead. Never invent a source.\"\n\nThis appears templated for numerical result extraction. \"REGISTERED TARGET\" in this case is natural-language target: \"the headline numerical result the paper states for its main finding\". They expect model identify headline number. Such prompts might be used to create replication targets, often headline result could be sample size? Need infer criteria: numerical result \"that a replication ... has to reproduce.\" The main finding should have an effect size. They emphasize value paper itself states. Unit printed. Could choose \"62 patients\" because exact cohort replication, but that isn't finding.\n\n\"headline number only\" could refer to article's headline result likely reported in title/abstract. Here no numeric in title. Abstract first numerical sentence is sample size 62, then 30 early recurrence. But main outcome result appears HRs. If someone asks headline number, likely hazard ratio.\n\nMaybe target is p value for \"intratumoral MDK was significantly overexpressed in early recurrence (p &lt; 0.001)\"—this is first main finding and directly tied to candidate biomarker. But replication should reproduce p&lt;0.001? Usually headline number in scientific abstract is effect estimate, not threshold. They might select \"62 patients\" as sample size. Yet wording \"main finding\" points to MDK expression and recurrence.\n\nCould produce parent_value 1.408 and robust context. Let's not overthink unless there is a hidden expected exact answer. Need perhaps unit \"HR\". Let's see schema property \"parent_value\" instead of \"value\" perhaps this comes from benchmarks where target has parent quantity. \"unit\" likely expected \"patients\", \"months\", \"%\", \"hazard ratio\". In oncology abstract, perhaps headline numerical result is \"62 patients\", unit patients. But \"single numerical result that a replication of the registered target has to reproduce\" might mean sample size target. Registered target \"the headline numerical result the paper states for its main finding\" likely generic enough that effect size. The paper states multiple p values and HRs, so \"single\" requires judgment.\n\nLet's examine title and conclusion. Main finding = \"MDK was considered a putative candidate for predicting early recurrence\". The result most directly supporting this is recurrence HR 1.408 in the study cohort, with adjusted 1.316. A replication of paper main finding likely reproduces \"intratumoral MDK expression significantly increased risk of recurrence (HR 1.408 ...).\" This is likely headline.\n\nCould target_result specify \"In the 62-patient stage IA LUAD cohort, intratumoral MDK expression significantly increased recurrence risk (univariate Cox HR 1.408).\" Evidence sentence. That's good.\n\nNeed not include number in target_result? Could, but headline only parent. Fine.\n\nLet's ensure JSON numbers no en dash issue okay string. Exact punctuation source uses (1.076–1.842), p = 0.013. Quote:\n\"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)\"\nThis exact substring appears. Does source sentence have \"and death\" directly, yes. Fine.\n\nUnit: \"hazard ratio\" appears exact. target_result can be source-derived. number_stated true.\n\nLet's answer only JSON.</think>\n\n{\"parent_value\":1.408,\"unit\":\"hazard ratio\",\"evidence\":\"Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)\",\"target_result\":\"Intratumoral MDK expression significantly increased the risk of recurrence in patients with stage IA lung adenocarcinoma.\",\"number_stated\":true}", "reading": {"parent_value": 1.408, "unit": "hazard ratio", "evidence": "Univariate Cox regression analysis indicated that intratumoral MDK expression significantly increased the risk of recurrence (hazard ratio [HR] 1.408 (1.076–1.842), p = 0.013)", "target_result": "Intratumoral MDK expression significantly increased the risk of recurrence in patients with stage IA lung adenocarcinoma.", "number_stated": true, "source_locator": "chars 947-1122 of source sha256 01bf1660c005255fc7ff6d56e4f8859468239ac88ddc9996248da34e69ec0143"}, "recorded_at": "2026-10-31T15:27:02.286218+00:00"}], "agreement": true, "disagreements": {"physical_methods": {"minimax": ["Intratumoral and peritumoral MDK expression were measured by immunohistochemistry staining.", "Surgical samples were collected from 62 patients with stage IA LUAD."], "minimax_alt": ["Collection of surgical samples from 62 patients with stage IA lung adenocarcinoma (LUAD)", "Immunohistochemistry staining of intratumoral and peritumoral MDK expression"], "decisive": false}, "analysis_methods": {"minimax": ["Kruskal-Wallis test", "Kaplan-Meier survival analysis", "log-rank test", "Univariate Cox regression", "Bidirectional stepwise Cox regression", "Cox proportional hazards assumption testing using cox.zph", "Two-sided statistical analysis"], "minimax_alt": ["Kruskal-Wallis test for H-score comparisons between groups", "Kaplan-Meier survival analysis", "Log-rank test", "Univariate Cox regression", "Bidirectional stepwise Cox regression", "Likelihood ratio test for variable inclusion", "Wald test for variable exclusion", "Proportional hazards assumption testing with cox.zph"], "decisive": false}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "passed", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:2ea04f6f290e9502c25a10dbc180da85a079166ea103092b4087064b4619e71a", "type": "sep_package", "label": "SEP v1 package for pmid:40740967"}], "activities": [{"id": "activity:conversion:pmid:40740967", "type": "conversion", "used": ["package:2ea04f6f290e9502c25a10dbc180da85a079166ea103092b4087064b4619e71a"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40740967", "parent_hash": "sha256:c545ec6a6bf30518f0eb621bcd59a3e6dce838d1ad087a01fed6977e32e373dd", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40740967 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40740967' does not resolve to a declared asset", "targets"]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T02:59:36+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}