{"data":{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39974411", "kind": "science-record", "revision": 2, "content_hash": "sha256:6ce55128b19ce7512748911f7b9b67e639e99e9480725b6a52771d4878eb1b16", "created_at": "2026-11-01T10:25:49Z", "updated_at": "2026-11-01T14:01:34Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Diagnostic value of a lactylation-related gene signature in hepatocellular carcinoma.", "doi": "10.21037/tcr-24-1023", "pmid": "39974411", "authors": null, "journal": "Translational cancer research", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "qualitative", "state": "blocked", "target": {"value": null, "unit": null, "tolerance": null, "source": null}, "evidence": []}], "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:39974411"], "outputs": ["the published estimate, its unit and its interval"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data behind the published result", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:not_downloaded", "description": "The published result cannot be recomputed without the data the paper analysed.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its provenance"], "instruments": [], 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"role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-1023", "sha256": "sha256:14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-24-1023", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}, {"id": "asset:table:pmid:39974411:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:3ef91910cc9fadf90a25ee6724e9c39515e57b0b2d9fba709d1ef0df9aeb866a", "bytes": 435, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[15]/table-wrap[1]#t1", "version": null, "note": "Gene signature characteristics from TCGA", "structure": {"pmcid": "PMC11833428", "section_index": 15, "section_title": "Gene selection", "row_count": 5, "column_count": 6}, "table": {"label": "Table 1", "caption": "Gene signature characteristics from TCGA", "rows": [["Variables", "β", "S.E.", "Wald", "OR", "P"], ["BSG", "0.079", "0.021", "13.535", "1.082", "0.001"], ["KAT2A", "0.730", "0.170", "18.370", "2.075", "0.001"], ["ZEB1", "1.190", "0.539", "4.882", "3.289", "0.03"], ["Constant", "−9.469", "1.760", "28.957", "0.000", "0.001"]], "row_count": 5, "column_count": 6, "numeric_cells": 20, "header_rows": 1, "locator": "section[15]/table-wrap[1]#t1"}}, {"id": "asset:table:pmid:39974411:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:cfb1fbdbae5d4e923e1e4c0dafb1aa25e3b3a8fc5039ec277f9d31a2276cc443", "bytes": 339, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[1]#t2", "version": null, "note": "The classification of TCGA and GEO data", "structure": {"pmcid": "PMC11833428", "section_index": 17, "section_title": "Prognostic analysis", "row_count": 3, "column_count": 5}, "table": {"label": "Table 2", "caption": "The classification of TCGA and GEO data", "rows": [["Sample type", "TCGA-LIHC (n=419)", "GSE14520 (n=43)", "GSE84402 (n=28)", "GSE62232 (n=91)"], ["Tumor", "369", "22", "14", "81"], ["Normal", "50", "21", "14", "10"]], "row_count": 3, "column_count": 5, "numeric_cells": 12, "header_rows": 1, "locator": "section[17]/table-wrap[1]#t2"}}, {"id": "asset:table:pmid:39974411:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:2e0473bd5bfbc9b4b0848c6db97c0ce7c394387df4ee3b3cc4b7922518a49ef9", "bytes": 608, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[2]#t3", "version": null, "note": "Details of TCGA data", "structure": {"pmcid": "PMC11833428", "section_index": 17, "section_title": "Prognostic analysis", "row_count": 29, "column_count": 2}, "table": {"label": "Table 3", "caption": "Details of TCGA data", "rows": [["Parameters", "TCGA-LIHC (n=363)"], ["Age (years)", ""], ["≤65", "136"], [">65", "227"], ["Survival status", ""], ["Death", "129"], ["Alive", "234"], ["Gender", ""], ["Male", "245"], ["Female", "118"], ["Stage", ""], ["I", "170"], ["II", "84"], ["III", "81"], ["IV", "4"], ["Pathologic_M", ""], ["M0", "262"], ["M1", "3"], ["MX", "98"], ["Pathologic_N", ""], ["N0", "246"], ["N1", "4"], ["NX", "113"], ["Pathologic_T", ""], ["T1", "180"], ["T2", "91"], ["T3", "76"], ["T4", "13"], ["TX", "1"]], "row_count": 29, "column_count": 2, "numeric_cells": 24, "header_rows": 1, "locator": "section[17]/table-wrap[2]#t3"}}], "gaps": ["asset:source:pmid:39974411 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:39974411", "asset:table:pmid:39974411:1", "asset:table:pmid:39974411:2", "asset:table:pmid:39974411:3"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:39974411"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "643be5fbdb2672b962b01feb5817e565143f69c173c267cb9871104995858ba1", "source_hash": "14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "raw_response_sha256": "sha256:d11ff3fbb1038a4dc659bf873169c86c69c400019052af2363029fc9ec069504", "raw_response_chars": 69694}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "8880c3cf85785a5e51bdad6aa25ba10d14dea807de2ff94f6b52e39bec05764c", "source_hash": "14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "raw_response": "{\"parent_value\":3,\"unit\":\"gene signatures\",\"evidence\":\"• Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\"target_result\":\"The paper states that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.\",\"number_stated\":true}", "reading": {"parent_value": 3, "unit": "gene signatures", "evidence": "• Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.", "target_result": "The paper states that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.", "number_stated": true, "source_locator": "chars 3683-3835 of source sha256 14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e"}, "recorded_at": "2026-10-31T15:31:04.811683+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "ff5e69504143e366699dab54bfe76bbfda9f193c2b29b8da6c76375b7f8063fa", "source_hash": "14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication of registered target headline numerical result paper states main finding. Source appears abstract/results but no obvious headline performance metric like AUC. Need inspect all numbers and infer target. Registered target generic \"headline numerical result the paper states for its main finding\". The paper key finding: \"Three lactylation-related diagnostic gene signatures predicted...\" Number three. Maybe headline numerical result? Could be three signatures. Need parse wording instruction: \"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.\"\n\nCould registered target correspond to hidden target concept likely \"headline numerical result paper states for its main finding\". Main finding perhaps three diagnostic gene signatures. Number = 3, unit perhaps \"lactylation-related diagnostic gene signatures\" or \"signatures\". Evidence exact contiguous quote entire bullet. Target result explain. But need determine likely intended numerical result. They may expect a number that is directly stated for main finding, not P values etc. Text has \"[Key findings] • Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\" This clearly has headline numerical result: Three. A replication of registered target likely reproduce three gene signatures. Unit as printed? \"Three lactylation-related diagnostic gene signatures\" — unit likely \"diagnostic gene signatures\" or \"lactylation-related diagnostic gene signatures\". parent_value 3.\n\nBut perhaps main finding's headline result is no AUC due truncated source, and instruction asks if no number set false. Could be target \"headline numerical result the paper states for its main finding\" and source only says \"good predictive capability\" without an AUC. Need identify registered target perhaps dynamically phrased in benchmark. We need understand task format. It asks target is \"the headline numerical result the paper states for its main finding\". Could refer to a specific numerical metric like AUC in abstract normally, but source doesn't include actual AUC. Maybe \"Three lactylation-related diagnostic gene signatures...\" is key finding, number 3. Is that a numerical result replication has to reproduce? Yes number of signatures. But \"headline number only\" perhaps asks for \"the value paper itself states for registered target\". Unit printed. Evidence exact quote.\n\nLet's inspect source details. Title. Abstract methods/results no quantitative result except datasets and versions, etc. Key findings has \"Three lactylation-related diagnostic gene signatures\". This is explicit count and main finding. Elsewhere risk signature consists BSG, KAT2A, ZEB1 and risk score (three genes plus score). Number \"22 varied immune cells\", \"11 types\". Main model perhaps 4 genes? The key finding explicitly says three signatures. Which is likely \"headline numerical result\"? The registered target wording isn't specific like \"number of diagnostic gene signatures\"; it literally says headline numerical result for main finding. Maybe benchmark generated target from paper's table? Need reason.\n\nThis user prompt likely part of extraction dataset where registered target often e.g. \"the AUC reported for X\". Here target is generic perhaps intentionally asks \"headline numerical result\". We should only extract direct number in headline/key finding. Number 3.\n\nUnit: \"lactylation-related diagnostic gene signatures\" perhaps unit must be unit paper prints. The paper prints \"Three lactylation-related diagnostic gene signatures\". JSON:\n{\n \"parent_value\":3,\n \"unit\":\"lactylation-related diagnostic gene signatures\",\n \"evidence\":\"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n \"target_result\":\"The paper states that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.\",\n \"number_stated\":true\n}\nBut instruction says report headline number only. target_result perhaps should not duplicate? Required target_result string. Could say \"Three lactylation-related diagnostic gene signatures...\" or \"The headline result is three...\" Evidence exact. Ensure evidence contains number exact. \"Three\" isn't numerical digit but number. parent value 3.\n\nYet perhaps unit should be \"signatures\" since count unit. They say unit must be unit paper prints. \"lactylation-related diagnostic gene signatures\" is noun phrase and paper prints. Could use \"gene signatures\". Is there concern \"Three\" is spelled out, and number exact contiguous? Yes.\n\nCould there be a more headline numeric result: P&lt;0.001 in prognostic analysis, perhaps higher risk score at T3 vs T1 and stage III vs I; list of 7 subgroup P values. But no singular headline result. Key findings bullet definitely headline.\n\nLet's not rush. Need provide valid JSON, no markdown. target_result likely concise. The source wording exact for evidence only; target can paraphrase perhaps. They explicitly ask evidence exact quote. Use whole clause. Could include bullet? No need. Evidence contiguous exact sentence.\n\nMaybe number_stated false because registered target = \"headline numerical result the paper states for its main finding\" but no actual quantitative performance metric is given. They emphasize \"If the text states no number for the registered target, set number_stated to false ... and say in target_result what the text does state instead.\" Why include this fallback? Perhaps source deliberately has a key finding with \"Three...\" so straightforward. But perhaps target refers to something like \"headline numerical result\" and they expect false due no AUC. Let's parse semantics closely.\n\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.\"\n\nRegistered target is not a metric; it's meta: \"the headline numerical result the paper states for its main finding\". So determine main finding and its headline numerical result. In paper source, [Key findings] bullet is explicitly headline and starts \"Three\". That likely is intended answer.\n\n\"The unit must be the unit the paper prints.\" Count of signatures unit. \"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.\"\n\nHere number directly attached: \"Three lactylation-related diagnostic gene signatures...\" Quote whole clause/sentence. So yes.\n\nCould \"Three\" refer to key findings bullet but maybe not a result to replicate? Definitely a model count. A replication would reproduce the identification of three signatures. It's main finding. Use unit perhaps \"lactylation-related diagnostic gene signatures\". Parent_value number 3.\n\nNeed maybe target_result = exact phrase \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\" But then evidence same. They ask \"headline number only\" probably parent_value and unit, while target_result can state target. Fine.\n\nLet's ensure JSON valid, no extra properties. Could set unit \"lactylation-related diagnostic gene signatures\" and target_result \"The main finding is that three lactylation-related diagnostic gene signatures predicted prognosis and immunotherapy response in HCC patients.\" The \"headline number only\" perhaps don't include extraneous numbers in target_result (HCC no number). Fine.\n\nLet's answer.</think>\n\n{\"parent_value\":3,\"unit\":\"lactylation-related diagnostic gene signatures\",\"evidence\":\"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\"target_result\":\"The main finding is that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.\",\"number_stated\":true}", "reading": {"parent_value": 3, "unit": "lactylation-related diagnostic gene signatures", "evidence": "Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.", "target_result": "The main finding is that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.", "number_stated": true, "source_locator": "chars 3685-3835 of source sha256 14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e"}, "recorded_at": "2026-10-31T15:31:17.484438+00:00"}], "agreement": false, "disagreements": {"unit": {"minimax": "gene signatures", "minimax_alt": "lactylation-related diagnostic gene signatures", "decisive": true}}, "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": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "reason": "a repository quotation is absent from the supplied text", "raw_response": "{\n  \"target_result\": \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n  \"work_type\": \"analysis_plus_physical_validation\",\n  \"physical_methods\": [\n    \"real-time quantitative polymerase chain reaction (RT-PCR)\"\n  ],\n  \"analysis_methods\": [\n    \"Differential analysis\",\n    \"random forest analysis\",\n    \"Receiver operating characteristic (ROC) curve analysis\",\n    \"logistic regression\",\n    \"ConsensusClusterPlus\",\n    \"single-sample gene set enrichment analysis (ssGSEA)\",\n    \"Kaplan-Meier (KM) analysis\",\n    \"log-rank test\",\n    \"oncoPredict\",\n    \"t-tests\"\n  ],\n  \"statistical_method\": \"Differential analysis and random forest analysis were used to derive the gene signatures, while logistic regression was used to obtain the risk-score equation and ROC curves were used to evaluate diagnostic efficiency.\",\n  \"method_evidence\": \"Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation.\",\n  \"repositories\": [\n    {\n      \"repository\": \"The Cancer Genome Atlas (TCGA)\",\n      \"accession\": \"TCGA\",\n      \"data_description\": \"The TCGA cohort was used for identification and analysis of the lactylation-related signatures, including risk-score stratification and survival analysis.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation. Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR).\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE14520\",\n      \"data_description\": \"HCC gene-expression dataset downloaded for analysis and used as part of the GEO validation resources for the diagnostic model.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE84402\",\n      \"data_description\": \"HCC gene-expression dataset downloaded for analysis and used as part of the GEO validation resources for the diagnostic model.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE62232\",\n      \"data_description\": \"HCC gene-expression dataset downloaded for analysis and used as part of the GEO validation resources for the diagnostic model.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n      \"publicly_downloadable\": false\n    }\n  ],\n  \"availability_statement\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets.\",\n  \"data_on_request\": false,\n  \"source_claims\": [\n    \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n    \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets.\",\n    \"Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC).\",\n    \"Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation.\",\n    \"Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR).\",\n    \"The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n    \"The patients with high risk score had worse outcomes than those with low risk score in the TCGA database.\",\n    \"ROC curves showed the characteristics of lactylation-related gene signature with a good predictive capability.\",\n    \"Finally, in terms of assessing the potency of immunotherapy in patients, the low risk score group had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a good response to immunotherapy.\"\n  ],\n  \"missing_information\": [\n    \"The supplied text does not report a numerical diagnostic performance value such as AUC, sensitivity, or specificity for the three-signature target result.\",\n    \"The supplied text does not explicitly state whether the repository data can be downloaded without an application, embargo, or request.\",\n    \"The supplied text does not explicitly identify a request-based or restricted access procedure.\",\n    \"The supplied text does not specify which individual GEO accession was used for each separate validation analysis.\",\n    \"The supplied text does not provide the physical RT-PCR samples or a repository accession for the experimentally generated expression data.\"\n  ],\n  \"evidence_spans\": [\n    \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n    \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets.\",\n    \"Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation.\",\n    \"Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR).\",\n    \"The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n    \"The patients with high risk score had worse outcomes than those with low risk score in the TCGA database.\",\n    \"ROC curves showed the characteristics of lactylation-related gene signature with a good predictive capability.\",\n    \"Finally, in terms of assessing the potency of immunotherapy in patients, the low risk score group had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a good response to immunotherapy.\"\n  ]\n}"}]}}, {"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.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:a2412edc10e95cb059c8d0229b73ec9416a88dcb07c4428b1463d3deb50f2eed", "type": "sep_package", "label": "SEP v1 package for pmid:39974411"}], "activities": [{"id": "activity:conversion:pmid:39974411", "type": "conversion", "used": ["package:a2412edc10e95cb059c8d0229b73ec9416a88dcb07c4428b1463d3deb50f2eed"], "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:39974411", "parent_hash": "sha256:dfa2646ad1a5b74652b1d94fc64eaab0264fe70e0add88622fe55470dcc0b70b", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:39974411 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "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:39974411' 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":"2026-11-04T02:50:23+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}}}