Machine learning-based construction of a programmed cell death-related model reveals prognosis and immune infiltration in pancreatic adenocarcinoma patients.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40646168", "kind": "science-record", "revision": 1, "content_hash": "sha256:5a6b2bed99bca98291e4dcbd21f14dca9098314653628ec79d90496cd1091806", "created_at": "2026-11-01T10:25:51Z", "updated_at": "2026-11-01T10:25:51Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Machine learning-based construction of a programmed cell death-related model reveals prognosis and immune infiltration in pancreatic adenocarcinoma patients.", "doi": "10.1038/s41598-025-10847-9", "pmid": "40646168", "authors": null, "journal": "Scientific 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": 6.0, "unit": "pcdrgs", "tolerance": null, 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sentence it came from.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40646168"], "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": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the analysis programme", "kind": "analysis", "state": "blocked", "blocked_on": "no owned computational capability is bound to this experiment", "description": "Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:40646168", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the registered result"], "instruments": [], "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:40646168' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40646168", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-10847-9", "sha256": "sha256:b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-10847-9", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40646168 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:40646168"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40646168"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "fdf84493d178edfa0d01cb50b82beeba36353ea27f04c3f5008f938fc1db3666", "source_hash": "b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc", "raw_response_sha256": "sha256:98dfabb5521e48d3ff9be7ffc449901825baa466f4cd59a5f4d0b195e94a21ac", "raw_response_chars": 6524}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "f850ddabfb534f5b4689314bbe25b848de13f27fe73bdc79f1b795754b314a18", "source_hash": "b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc", "raw_response_sha256": "sha256:83d6041be1ff0f6781afeeb381922285527a220f191d3b18fe7349bdbef9acad", "raw_response_chars": 78380}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "29da7a6684309e5d6a339269d5a74a1293581e8048a3b146a45e4ef1e3d312e9", "source_hash": "b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc", "raw_response": "{\"parent_value\":6,\"unit\":\"PCDRGs\",\"evidence\":\"Using a comprehensive machine learning framework involving 117 algorithmic combinations under a Leave-one-out cross-validation (LOOCV) strategy, we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\",\"target_result\":\"The best prognostic model was based on six PCDRGs: ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "PCDRGs", "evidence": "Using a comprehensive machine learning framework involving 117 algorithmic combinations under a Leave-one-out cross-validation (LOOCV) strategy, we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.", "target_result": "The best prognostic model was based on six PCDRGs: ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.", "number_stated": true, "source_locator": "chars 648-962 of source sha256 b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc"}, "recorded_at": "2026-10-31T15:27:09.905628+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "6cb8dea799fa71590530c8a599a1f56bd4a322cfa78d499a62d4850041236361", "source_hash": "b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline for main finding, value paper itself states registered target. Source abstract and excerpts. Need infer main finding perhaps six PCDRGs? Headline numerical result model based on six genes. \"headline numerical result the paper states for its main finding\" Replication target likely number 6? Need parse request wording. Registered target generic: headline numerical result paper states for main finding. Need report headline number only, paper stated, unit printed, exact contiguous quotation containing number. Candidate numerical results:\n- 1,034 samples integrated\n- 17 candidate genes\n- 117 algorithmic combinations\n- six PCDRGs named\n- risk score no metric\n- k=2 clusters\n- P=0.01\n- 1-,3-,5-year survival prediction (three numbers, not single?)\nWhat is \"headline numerical result\" likely abstract's main finding: model based on six PCDRGs. Perhaps replication of registered target needs reproduce six genes? But unit \"PCDRGs\". They ask numerical result. Need identify likely expected answer based on source extraction benchmark. \"single numerical result that a replication of the registered target has to reproduce.\" Registered target is \"the headline numerical result the paper states for its main finding.\" Could mean determine primary target from text, perhaps \"six PCDRGs\" in model construction. We need quote whole clause attribution. Likely parent_value 6, unit \"PCDRGs\", evidence exact sentence/clause: \"Using a comprehensive machine learning framework involving 117 algorithmic combinations under a Leave-one-out cross-validation (LOOCV) strategy, we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\" That contains both 117 and six; evidence must contain number and attribution. Quote whole clause. But \"single numerical result\" perhaps 117 algorithmic combinations? Main finding could be \"six PCDRGs\" and exact number. Need choose.\n\nLet's examine wording. They expect perhaps output based on \"headline number\" extraction from paper. Registered target description likely points to main abstract claim. Main finding: constructed model based on six PCDRGs. \"A nomogram ... 1-, 3-, and 5-year survival prediction.\" Could headline numerical result be six genes. The word \"headline numerical result\" often asks for main result metric, e.g. AUC = 0.XX. But supplied text omits detailed results with performance metrics. Maybe abstract has counts. Need not calculate. They ask \"single numerical result that a replication of the registered target has to reproduce.\" Could be target is perhaps \"number of genes in signature\" (six). There is no explicit performance number. We should set false if no number for registered target? Yet registered target itself says headline numerical result. Need extract one.\n\nLet's understand likely dataset/task design. User provides source text and asks numerical result with registered target. The target may be abstract semantic slot. They insist \"value paper itself states for registered target, not calculated, converted, averaged or reconstructed.\" Evidence exact contiguous quote. If no number, false. We need likely identify one main result. The abstract's key numerical output could be \"model based on six PCDRGs\". Unit paper prints likely \"PCDRGs\". \"headline number only\" likely they want 6 PCDRGs. The evidence could quote whole clause to check attribution. Need JSON number 6.\n\nCould candidate be 17 candidate PCDRGs associated with OS; model built on six. Main finding ultimately signature based on six. \"headline numerical result\" perhaps best model selected among 117 combos, based on six genes. The final model's headline is six-gene signature. So answer likely 6, unit \"PCDRGs\", exact quote. target_result concise: \"The paper states that the selected prognostic model was based on six PCDRGs.\" number_stated true.\n\nNeed ensure exact contiguous quotation. Could quote full sentence exactly, though starts \"Using...\" and includes number 117. They request headline number only but evidence can have other number; evidence quote whole clause. Maybe quote only \"we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\" Is that exact contiguous clause and contains attribution. It includes only six numeric digit, and gene names. The source sentence begins \"Using...\" We can quote from \"we identified...\" Exact. Is \"whole clause\" enough. Could quote full sentence including 117. Better exact quote from source to establish \"we identified ... based on six PCDRGs\". Their instruction: \"When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\" Clause could be entire sentence. But including 117 could create ambiguity for \"single numerical result\". Better quote only predicate clause after introductory phrase. Exact contiguous: \"we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\" This is a clause, no other numerical result. Great.\n\nUnit: Does paper print \"six PCDRGs\"; unit perhaps \"PCDRGs\". They ask unit must be unit paper prints. Use \"PCDRGs\". parent_value 6. target_result perhaps \"The best-performing prognostic model was based on six PCDRGs.\" We should not overcomplicate. JSON valid, no markdown.\n\nBut let's thoroughly assess possibility target result = 1,034 samples. \"headline numerical result paper states for its main finding\" Main finding perhaps cohort size? A replication of main finding would need reproduce 1,034 samples? No, replication doesn't reproduce sample count necessarily. Six-gene model also would reproduce gene composition, not a numerical outcome. A headline numerical result usually effect estimate or performance metric. Source lacks such. Could answer false: no number stated for registered target? The source text says robust predictive performance, high-risk worse prognosis, etc., but no metric. User specifically says \"If the text states no number for the registered target, set number_stated to false ... say in target_result what the text does state instead.\" Could this be trap: abstract has various counts, but no headline numerical result like AUC or HR for main finding. Registered target likely asks generic main finding, and source excerpts perhaps intentionally don't include metrics. We need discern.\n\nLet's parse first instruction semantically: \"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.\" REGISTERED TARGET: \"the headline numerical result the paper states for its main finding\". This seems tautological. They might expect extracting a number like sample count, gene count, clusters. The \"single numerical result\" likely defined in article metadata elsewhere, but target field is natural-language abstraction. The supplied source includes abstract + methods/results snippets. Need locate likely headline number.\n\nCould be from abstract's result: \"identified 17 candidate PCD-related genes significantly associated with overall survival\" then machine learning identifies model based on six genes. Main finding's number perhaps 6. The \"registered target\" wording may be generated to avoid naming target explicitly, but description is clear enough: headline numerical result for main finding = six PCDRGs. They want only headline number, perhaps not all numeric claims. So answer 6.\n\nLet's look at paper title and abstract. Main finding: machine-learning construction of programmed cell death-related model reveals prognosis and immune infiltration. Key quantitative element: \"model based on six PCDRGs\". That's indeed model composition, central headline. Other abstract numbers: 1,034 samples, 17 candidates, 117 algorithmic combinations, 1/3/5 years. Which is \"headline numerical result\"? Usually abstract conclusion: established prognostic signature, no numeric performance. The distinctive main output is six genes. Replication of registered target perhaps replicating \"six-gene model\". So yes.\n\nUnit perhaps \"PCDRGs\" not \"genes\". They specify \"unit must be the unit the paper prints.\" Quote says \"six PCDRGs\". Good.\n\nEvidence exact contiguous quote. Let's make sure punctuation and Unicode. Source:\n\"Using a comprehensive machine learning framework involving 117 algorithmic combinations under a Leave-one-out cross-validation (LOOCV) strategy, we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\"\nWe'll quote from \"we identified...\" exact, perhaps ending period. Exact contiguous. The evidence requirement says exact contiguous quotation from SOURCE TEXT. JSON escape no issues. Use this.\n\ntarget_result: \"The paper states that the best algorithms composition constructed a prognostic model based on six PCDRGs.\" Should headline number only maybe target_result should state result with number? Schema likely expected explanatory. Could say \"Prognostic model based on six PCDRGs.\" But instruction \"Report the headline number only\" applies parent_value perhaps. target_result can summarize. Could avoid number because headline number separately. Maybe \"The prognostic model is based on six PCDRGs.\" Number stated true. JSON number 6.\n\nNeed ensure no markdown.</think>\n\n{\"parent_value\":6,\"unit\":\"PCDRGs\",\"evidence\":\"we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.\",\"target_result\":\"The selected prognostic model was based on six PCDRGs.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "PCDRGs", "evidence": "we identified the StepCox[both] + Ridge as the best algorithms composition to construct a prognostic model based on six PCDRGs, ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.", "target_result": "The selected prognostic model was based on six PCDRGs.", "number_stated": true, "source_locator": "chars 793-962 of source sha256 b7e9f327d2fd0dbe9ba3f932f4561e6c0bcd6773363bc2dd890bf0d1860567cc"}, "recorded_at": "2026-10-31T15:27:24.070491+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A prognostic model based on six PCDRGs—ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU—was selected from 17 candidate genes after evaluating 117 algorithmic combinations under a Leave-one-out cross-validation (LOOCV) strategy.", "minimax_alt": "The StepCox[both] + Ridge algorithm composition, selected from 117 algorithmic combinations under leave-one-out cross-validation, was used to construct a prognostic model based on six PCDRGs: ITGA3, CDCP1, IL1RAP, CLU, PBK, and PLAU.", "decisive": false}, "physical_methods": {"minimax": [], "minimax_alt": ["qRT-PCR"], "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis", "Univariate Cox regression analysis", "Leave-one-out cross-validation (LOOCV)", "StepCox[both] + Ridge", "Kaplan-Meier survival analysis", "Log-rank test", "Multivariate Cox proportional hazards regression", "Unsupervised consensus clustering", "GO enrichment analysis", "KEGG pathway enrichment analysis", "GSVA", "Shapiro-Wilk test", "Student’s t-test", "One-way analysis of variance (ANOVA)", "Wilcoxon rank-sum test", "Mann-Whitney U test"], "minimax_alt": ["Differential expression analysis", "Univariate Cox regression analysis", "Leave-one-out cross-validation (LOOCV)", "StepCox[both] + Ridge", "Kaplan-Meier survival analysis with the log-rank test", "Univariate and multivariate Cox proportional hazards regression"], "decisive": false}, "statistical_method": {"minimax": "Leave-one-out cross-validation (LOOCV)", "minimax_alt": "Leave-one-out cross-validation (LOOCV) with the StepCox[both] + Ridge algorithmic composition", "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": []}}, {"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:e0708e49cafcfd12ab2f377be17c458d00fd9abbadb1658cf8534411040038c9", "type": "sep_package", "label": "SEP v1 package for pmid:40646168"}], "activities": [{"id": "activity:conversion:pmid:40646168", "type": "conversion", "used": ["package:e0708e49cafcfd12ab2f377be17c458d00fd9abbadb1658cf8534411040038c9"], "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": "48b1e2d6-012e-4b25-b3f5-76256875205d", "parent_hash": "e0708e49cafcfd12ab2f377be17c458d00fd9abbadb1658cf8534411040038c9", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40646168 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:40646168' does not resolve to a declared asset", "targets"]}