{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41618052", "kind": "science-record", "revision": 3, "content_hash": "sha256:c3f5e8a215e0bbd1dc4e2a18d55f19b4176874187d66f0f740fbd5ddc8fd0228", "created_at": "2026-11-01T20:31:43Z", "updated_at": "2026-11-01T22:15:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.", "doi": "10.1007/s12672-026-04538-8", "pmid": "41618052", "authors": ["Hui Zhang", "Huan Su", "Kaimei Xia", "Haiyan Wang", "Yi Wu", "Yan Xiong", "Ting Deng", "Ping Du"], "journal": "Discover oncology", "year": 2026, "abstract": "PURPOSE: This study aims to construct a prognostic model for hepatocellular carcinoma (HCC) based on palmitoylation-related genes and explore its molecular mechanisms through multi-dimensional analyses.\nMETHODS: The research integrated single-cell transcriptome data (GSE189903) with bulk transcriptome data (TCGA-LIHC, GEO datasets), focusing on palmitoylation-related genes in HCC epithelial cells. The scAB deconvolution algorithm was used to analyze the association between epithelial cell subsets and patient survival, and hdWGCNA was combined to construct a gene co-expression network. Through differential expression analysis, univariate Cox regression, and LASSO penalized regression, 7 key genes (SERPINE1, FMO3, ALDH2, CPS1, SLCO1B1, ACAT1, ACADS) were identified to build a prognostic risk model.\nRESULTS: Validation results showed that the model could effectively distinguish the survival prognosis of high-risk and low-risk patients (AUC values for 1/3/5 years in the TCGA cohort were 0.676, 0.656, and 0.642, respectively; those in the GSE14520 validation set were 0.702, 0.658, and 0.654, respectively), and the risk score was an independent prognostic factor. Further analyses revealed that the risk score was associated with tumor staging, immune cell infiltration (e.g., T cells, monocytes), response to immunotherapy, and drug sensitivity. Functional enrichment analysis indicated that the high-risk group was enriched in cell cycle regulation and oncogenic signaling pathways, while the low-risk group was related to metabolic pathways.\nCONCLUSION: This study is the first to analyze the regulatory network of palmitoylation in HCC epithelial cells by combining single-cell and bulk transcriptomes, providing new molecular targets and methodological references for HCC prognosis evaluation and precision therapy.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis and the “glmnet” package in R were applied to construct a gene signature for predicting OS using hepatocellular carcinoma-related palmitoylation candidate genes, with the candidate genes as independent variables and OS as the response variable [30].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41618052", "locator": "sections[6] \"Prognostic analysis of gene characterization\" paragraphs 1-3", "quote": "Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis and the “glmnet” package in R were applied to construct a gene signature for predicting OS using hepatocellular carcinoma-related palmitoylation candidate genes, with the candidate genes as independent variables and OS as the response variable [30]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41618052", "locator": "sections[35] \"notes\" paragraph 1", "quote": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Evaluation of the model’s accuracy in predicting outcomes showed that the 1-year AUC was 0.676, 3-year AUC was 0.656, and 5-year AUC was 0.642 (Fig. 6A).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41618052", "locator": "sections[20] \"Predictive efficacy of liver cancer prognostic risk model in training and validation sets\" paragraph 2", "quote": "Evaluation of the model’s accuracy in predicting outcomes showed that the 1-year AUC was 0.676, 3-year AUC was 0.656, and 5-year AUC was 0.642 (Fig. 6A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Evaluation of the model’s accuracy in predicting outcomes showed that the 1-year AUC was 0.676, 3-year AUC was 0.656, and 5-year AUC was 0.642 (Fig. 6A).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41618052"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE115018"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE115018"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41618052", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-026-04538-8", "sha256": "sha256:a147aca7374f19e51a281a39201eeea8afbf7c38cb91bc8f83406ddd2355cef4", "bytes": 64827, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-026-04538-8", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12873020", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE115018", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115018", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE115018", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3163056", "GSM3163057", "GSM3163058", "GSM3163059", "GSM3163060", "GSM3163061", "GSM3163062", "GSM3163063", "GSM3163064", "GSM3163065", "GSM3163066", "GSM3163067", "GSM3163068", "GSM3163069", "GSM3163070", "GSM3163071", "GSM3163072", "GSM3163073", "GSM3163074", "GSM3163075", "GSM3163076", "GSM3163077", "GSM3163078", "GSM3163079"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "20115", "taxon": "Homo sapiens", "title": "Simultaneous control of non-coding RNA on the G2/M phase of the cell cycle in hepatocellular carcinoma [lncRNA and mRNA]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE14520", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). 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The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). 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All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5709304", "GSM5709305", "GSM5709306", "GSM5709307", "GSM5709308", "GSM5709309", "GSM5709310", "GSM5709311", "GSM5709312", "GSM5709313", "GSM5709314", "GSM5709315", "GSM5709316", "GSM5709317", "GSM5709318", "GSM5709319", "GSM5709320", "GSM5709321", "GSM5709322", "GSM5709323", "GSM5709324", "GSM5709325", "GSM5709326", "GSM5709327", "GSM5709328", "GSM5709329", "GSM5709330", "GSM5709331", "GSM5709332", "GSM5709333", "GSM5709334", "GSM5709335", "GSM5709336", "GSM5709337"], "sample_count": 34, "group_sizes": null, "group_sizes_basis": null, "platform": "24676", "taxon": "Homo sapiens", "title": "Multiregional single-cell transcriptomic analysis of liver cancer", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE115018 not downloaded", "asset:data:geo:GSE115018 licence not read", "asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE189903 not downloaded", "asset:data:geo:GSE189903 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis and the “glmnet” package in R were applied to construct a gene signature for predicting OS using hepatocellular carcinoma-related palmitoylation candidate genes, with the candidate genes as independent variables and OS as the response variable [30]."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "differential_expression", "specific": "deseq2", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "DESeq2"}}, {"id": "method-4", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-5", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-6", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE189903", "GSE115018", "GSE14520"], "resolved_accessions": ["GSE189903", "GSE115018", "GSE14520"], "statements": ["The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, with the accession numbers GSE189903 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE189903), GSE14520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE14520) and GSE115018 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE115018). All microarray data comply with the MIAME guidelines. The TCGA-LIHC cohort data (transcriptomic and clinical information) are available in the University of California Santa Cruz (UCSC) Xena browser (https://xenabrowser.net/), with the dataset identifier TCGA-LIHC. The palmitoylation-related genes used in this study were retrieved from the GeneCards database (https://www.genecards.org/) using the keyword ‘Palmitoylation’. The protein expression data used for validation are available in the Human Protein Atlas (HPA) repository (https://www.proteinatlas.org/). All datasets are publicly accessible without restrictions."], "basis": "The paper names GSE189903, GSE115018, GSE14520 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:41618052", "data:geo:GSE115018"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE115018"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:12:16Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15515, "completion_tokens": 3061, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:41618052", "type": "paper", "label": "pmid:41618052"}, {"id": "source:pmid:41618052", "type": "manuscript", "label": "PMID 41618052 full text"}, {"id": "data:geo:GSE115018", "type": "dataset", "label": "GEO GSE115018"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41618052", "type": "paper", "label": "pmid:41618052"}, {"id": "source:pmid:41618052", "type": "manuscript", "label": "PMID 41618052 full text"}, {"id": "data:geo:GSE115018", "type": "dataset", "label": "GEO GSE115018"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41618052", "type": "extraction", "used": ["source:pmid:41618052"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE115018", "type": "accession_resolution", "used": ["data:geo:GSE115018"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:41618052", "parent_hash": "sha256:ec1b9d51724fd3557269cfedf7f6f23cbbee207db70a1868505ca7164bb651d6", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE115018 licence not read", "asset:data:geo:GSE115018 not downloaded", "asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE189903 licence not read", "asset:data:geo:GSE189903 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["mine the cutoff value of the risk score, and patients were divided into high-risk or low-risk groups [3"], "order_basis": "paper_text_order:3145", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Univariate Cox regression analysis was used to identify candidate genes associated with overall survival (OS). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis and the “glmnet” package in R were applied to construct a gene signature for predicting OS using hepatocellular carcinoma-related palmitoylation candidate genes, with the candidate genes as independent variables and OS as the response variable [30]."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]},"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":"2026-11-04T00:29:58+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}}}