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pmid:40853516
Identification of novel cholesterol metabolism-related biomarkers for thyroid cancer to predict the prognosis, immune infiltration, and drug sensitivity.
Discover oncology · 2025 doi:10.1007/s12672-025-03483-2
Paper
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Experiment
partial
Data
partial
Analysis
absent
Execution
absent
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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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Complete file
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"full-text-reading-v1", "prompt_hash": "02b8f2e431a4054095c83633a2f956831a33f4776f0ff8c58512d0d4def5c374", "source_hash": "a1d56d99a12618e6a050a735de8fe8cc9fb9b188f8f1dcf49964ea80b495c1ba", "raw_response_sha256": "sha256:4070b89a8d0a4a918f67f94496f81fa5ab36457fa7a32e4e38d45ccd8c11dec2", "raw_response_chars": 3919}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "5df956a123d40d499b05f58093f51874cce1980372be748c5ad55f768c394e38", "source_hash": "a1d56d99a12618e6a050a735de8fe8cc9fb9b188f8f1dcf49964ea80b495c1ba", "raw_response_sha256": "sha256:ac5d8f07a8550793ecbaaa604aca9c8ab7916504bdce27c126c34a081ccf8f7b", "raw_response_chars": 65009}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "6ba719aba2c8425c9bcfbce5279ccf9d12dd3a0cf904e65c36347b496676cbc8", "source_hash": "a1d56d99a12618e6a050a735de8fe8cc9fb9b188f8f1dcf49964ea80b495c1ba", "raw_response": "{\"parent_value\":11.41,\"unit\":\"times\",\"evidence\":\"We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced.\",\"target_result\":\"The mortality rate of high-risk patients increased by 11.41 times.\",\"number_stated\":true}", "reading": {"parent_value": 11.41, "unit": "times", "evidence": "We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced.", "target_result": "The mortality rate of high-risk patients increased by 11.41 times.", "number_stated": true, "source_locator": "chars 1780-1947 of source sha256 a1d56d99a12618e6a050a735de8fe8cc9fb9b188f8f1dcf49964ea80b495c1ba"}, "recorded_at": "2026-10-31T15:31:46.093664+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "206e76512645b4c66f554c035b7926805cd9dac7b3df10ef547305cfb0b90489", "source_hash": "a1d56d99a12618e6a050a735de8fe8cc9fb9b188f8f1dcf49964ea80b495c1ba", "raw_response": "<think>We need answer JSON object only. Need identify single numerical result headline main finding registered target. Paper abstract: \"We found that the mortality rate of high-risk patients increased by 11.41 times\" likely headline numerical result. Need report value, unit as printed. Unit? \"times\" perhaps unit. Evidence exact contiguous quote containing number, whole clause so attribution check. Need likely quote: \"We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced.\" parent_value 11.41, unit \"times\". target_result maybe \"The mortality rate of high-risk patients increased by 11.41 times.\" number_stated true.\n\nBut let's carefully parse instructions. \"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 text's own wording: a number may be attached to target by parenthetical list, by word 'respectively', or by words that name target directly. When that is how text links them, quote whole clause so reader can check attribution.\"\n\nHeadline main finding perhaps number of CMRGs = 6? Or mortality increased 11.41 times. \"headline numerical result the paper states for its main finding\" likely abstract's quantitative outcome. Need choose. The paper title main finding biomarkers, six genes. But single numerical result replication target. Could be 11.41 times as headline quantitative result. They ask unit paper prints. likely \"times\". The answer schema parent_value maybe 11.41. Evidence quote exact. target_result concise. Ensure JSON valid, no markdown.\n\nCould unit be \"times\" or \"fold\"? Paper prints \"times\", so times. Exact quote could include sentence. Evidence should quote whole clause. \"We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced.\" Exact contiguous. Target result perhaps \"mortality rate of high-risk patients increased by 11.41 times\". The \"headline number only\" perhaps target_result shouldn't include other results. Fine.\n\nNeed no extra prose.</think>\n\n{\"parent_value\":11.41,\"unit\":\"times\",\"evidence\":\"We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced.\",\"target_result\":\"The mortality rate of high-risk patients increased by 11.41 times.\",\"number_stated\":true}", "reading": {"parent_value": 11.41, "unit": "times", "evidence": "We found that the mortality rate of high-risk patients increased by 11.41 times, and the infiltration of immune cells in the high-risk group was significantly reduced.", "target_result": "The mortality rate of high-risk patients increased by 11.41 times.", "number_stated": true, "source_locator": "chars 1780-1947 of source sha256 a1d56d99a12618e6a050a735de8fe8cc9fb9b188f8f1dcf49964ea80b495c1ba"}, "recorded_at": "2026-10-31T15:31:48.398834+00:00"}], "agreement": false, "disagreements": {"analysis_methods": {"minimax": ["univariate Cox regression", "least absolute shrinkage and selection operator (LASSO)", "Lasso-Cox regression", "nomogram", "differential expression analysis using DESeq2", "Wilcoxon test", "KEGG and GO enrichment analysis", "gene set enrichment analysis (GSEA)", "single-sample gene set enrichment analysis (GSVA)", "ssGSEA", "Estimate, EPIC, CIBERSORT, and MCPcounter immune-cell estimation", "pRRophetic", "Wilcoxon signed-rank test"], "minimax_alt": ["univariate Cox regression", "least absolute shrinkage and selection operator (LASSO)", "Lasso-Cox regression", "random training/validation split", "nomogram", "DESeq2 differential-expression analysis", "Wilcoxon test", "KEGG pathway enrichment analysis", "Gene Ontology functional annotation", "Gene Set Enrichment Analysis (GSEA)", "Gene Set Variation Analysis (GSVA)", "single-sample Gene Set Enrichment Analysis (ssGSEA)", "Estimate", "EPIC", "CIBERSORT", "MCPcounter", "pRRophetic", "Wilcoxon signed-rank test"], "decisive": false}, "statistical_method": {"minimax": "Lasso-Cox regression", "minimax_alt": "univariate Cox regression and least absolute shrinkage and selection operator (LASSO)", "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:94166ddab9d69156712e9fbff0a47f3b30b23afab04f668aa4b1923830657d2b", "type": "sep_package", "label": "SEP v1 package for pmid:40853516"}], "activities": [{"id": "activity:conversion:pmid:40853516", "type": "conversion", "used": ["package:94166ddab9d69156712e9fbff0a47f3b30b23afab04f668aa4b1923830657d2b"], "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:40853516", "parent_hash": "sha256:da0d91976405a65061b10174808199bb460828b615bf7f0321bf6349e751ea22", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40853516 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:40853516' does not resolve to a declared asset", "targets"]}