{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39228680", "kind": "science-record", "revision": 4, "content_hash": "sha256:092a8625d5d40fdb1fa0e67737453273cd8efc57f3a02417d85e1f101095b131", "created_at": "2026-11-01T20:29:22Z", "updated_at": "2026-11-01T22:55:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying and Validating Extracellular Matrix-Related Gene CTSH in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning.", "doi": "10.2147/JIR.S467507", "pmid": "39228680", "authors": ["Pei-Yu Wu", "Yan-Lin Yu", "Wen-Rui Zhao", "Bo Zhou"], "journal": "Journal of inflammation research", "year": 2024, "abstract": "Background: Diabetic foot ulcer (DFU) is a serious clinical problem with high amputation and mortality rates, yet there is a lack of desirable therapy. While the extracellular matrix (ECM) contributes significantly to wound healing, ECM-related biomarker for DFU is still unknown. The study was designed to identify ECM-related biomarker in DFU using bioinformatics and machine learning and validate it in STZ-induced mice models.\nMethods: GSE80178 and GSE134431 microarray datasets were fetched from the GEO database, and differentially expressed genes (DEGs) analysis was performed, respectively. By analyzing DEGs and ECM genes, we identified ECM-related DEGs, and functional enrichment analysis was conducted. Subsequently, three machine learning algorithms (LASSO, RF and SVM-RFE) were applied to filter ECM-related DEGs to identify key ECM-related biomarkers. Next, we conducted immune infiltration analysis, GSEA, and correlation analysis to explore the hub gene underlying mechanism. A lncRNA-miRNA-mRNA and drug regulatory network were constructed. Finally, we validated the key ECM-related biomarker in STZ-induced mice models.\nResults: One hundred and forty-five common DEGs in adult DFU between the two datasets were identified. Taking the intersection of 145 common DEGs and 964 ECM genes, we identified 13 ECM-related DEGs. Thirteen ECM-related DEGs were mainly enriched in pathways associated with tissue remodeling, inflammation and defense against infectious agents. Ultimately, CTSH was identified as the key ECM-related biomarker. CTSH was associated with difference immune cells during the occurrence and development of DFU, and it influenced hedgehog, IL-17 and TNF signaling pathway. Additionally, CTSH expression is correlated with many ECM- and immune-related genes. A lncRNA-miRNA-mRNA and drug regulatory network were constructed with 10 lncRNAs, 2 miRNAs, CTSH and 1 drug. Finally, CTSH was validated as a key biomarker for DFU in animal models.\nConclusion: Our study found that CTSH can be used for both diagnostic and prognostic purposes and might be a potential therapeutic target.", "claims": [{"id": "claim-method", "statement": "Hub genes were analyzed using three machine learning methods (LASSO, RF and SVM-RFE).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39228680", "locator": "sections[6] \"Hub Genes Identification via Machine Learning Methods\" paragraph 2", "quote": "Hub genes were analyzed using three machine learning methods (LASSO, RF and SVM-RFE)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39228680", "locator": "sections[28] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CTSH was identified as the key ECM-related biomarker for diabetic foot ulcer through machine-learning analysis and validated in animal models.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39228680", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Ultimately, CTSH was identified as the key ECM-related biomarker."}, {"asset": "source:pmid:39228680", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Finally, CTSH was validated as a key biomarker for DFU in animal models."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CTSH was identified as the key ECM-related biomarker for diabetic foot ulcer through machine-learning analysis and validated in animal models.", "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:39228680"], "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:GSE134431"], "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:GSE134431"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39228680", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S467507", "sha256": "sha256:42a333b7f9507b8d76caebfb7b0936a2f300b07ac4d9095c5bf9f55b8736c21d", "bytes": 41352, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JIR.S467507", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11370762", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE134431", "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=GSE134431", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE134431", "version": null, "note": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3946485", "GSM3946486", "GSM3946487", "GSM3946488", "GSM3946489", "GSM3946490", "GSM3946491", "GSM3946492", "GSM3946493", "GSM3946494", "GSM3946495", "GSM3946496", "GSM3946497", "GSM3946498", "GSM3946499", "GSM3946500", "GSM3946501", "GSM3946502", "GSM3946503", "GSM3946504", "GSM3946505"], "sample_count": 21, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "Deregulated immune signature orchestrated by FOXM1 impairs human diabetic wound healing", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE147890", "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=GSE147890", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE147890", "version": null, "note": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4448280", "GSM4448281", "GSM4448282", "GSM4448283", "GSM4448284", "GSM4448285", "GSM4448286", "GSM4448287", "GSM4448288", "GSM4448289", "GSM4448290", "GSM4448291", "GSM4448292", "GSM4448293", "GSM4448294", "GSM4448295", "GSM4448296", "GSM4448297", "GSM4448298", "GSM4448299", "GSM4448300", "GSM4448301", "GSM4448302", "GSM4448303", "GSM4448304", "GSM4448305"], "sample_count": 26, "group_sizes": null, "group_sizes_basis": null, "platform": "571", "taxon": "Homo sapiens", "title": "Transcriptomic characterization of the delayed wound healing response in a diabetic skin humanized mice model", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE23006", "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=GSE23006", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE23006", "version": null, "note": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM567618", "GSM567619", "GSM567620", "GSM567621", "GSM567622", "GSM567623", "GSM567624", "GSM567625", "GSM567626", "GSM567627", "GSM567628", "GSM567629", "GSM567630", "GSM567631", "GSM567632", "GSM567633", "GSM567634", "GSM567635", "GSM567636", "GSM567637", "GSM567638", "GSM567639", "GSM567640", "GSM567641", "GSM567642", "GSM567643", "GSM567644", "GSM567645", "GSM567646", "GSM567647", "GSM567648", "GSM567649", "GSM567650", "GSM567651", "GSM567652", "GSM567653", "GSM567654", "GSM567655", "GSM567656", "GSM567657", "GSM567658", "GSM567659", "GSM567660", "GSM567661", "GSM567662", "GSM567663", "GSM567664", "GSM567665"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "1261", "taxon": "Mus musculus", "title": "Transcriptional profiling of a  wound healing process in skin and oral mucosa", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE80178", "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=GSE80178", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE80178", "version": null, "note": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2114231", "GSM2114232", "GSM2114233", "GSM2114234", "GSM2114235", "GSM2114236", "GSM2114237", "GSM2114238", "GSM2114239", "GSM2114240", "GSM2114241", "GSM2114242"], "sample_count": 12, "group_sizes": null, "group_sizes_basis": null, "platform": "16686", "taxon": "Homo sapiens", "title": "Genomic Profiling of Diabetic Foot Ulcers Identifies miR-15b-5p as a Major Regulator that Leads to Suboptimal Inflammatory Response and Diminished DNA Repair Mechanisms", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE134431 not downloaded", "asset:data:geo:GSE134431 licence not read", "asset:data:geo:GSE147890 not downloaded", "asset:data:geo:GSE147890 licence not read", "asset:data:geo:GSE23006 not downloaded", "asset:data:geo:GSE23006 licence not read", "asset:data:geo:GSE80178 not downloaded", "asset:data:geo:GSE80178 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Hub genes were analyzed using three machine learning methods (LASSO, RF and SVM-RFE)."}}, {"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": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "random Forest"}}, {"id": "method-4", "family": "machine_learning", "specific": "decision_tree", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "decision tree"}}], "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": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: support_vector_machine."}, "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": ["GSE80178", "GSE134431", "GSE147890", "GSE23006"], "resolved_accessions": ["GSE80178", "GSE134431", "GSE147890", "GSE23006"], "statements": ["The datasets analyzed in this article are accessible to the public. The datasets were acquired from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo/) database, and the accession numbers were GSE80178, GSE134431, GSE147890 and GSE23006."], "basis": "The paper names GSE80178, GSE134431, GSE147890, GSE23006 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": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:39228680", "data:geo:GSE134431"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE134431"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:30:16Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11620, "completion_tokens": 2554, "cached_tokens": 264}}, "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:39228680", "type": "paper", "label": "pmid:39228680"}, {"id": "source:pmid:39228680", "type": "manuscript", "label": "PMID 39228680 full text"}, {"id": "data:geo:GSE134431", "type": "dataset", "label": "GEO GSE134431"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39228680", "type": "paper", "label": "pmid:39228680"}, {"id": "source:pmid:39228680", "type": "manuscript", "label": "PMID 39228680 full text"}, {"id": "data:geo:GSE134431", "type": "dataset", "label": "GEO GSE134431"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39228680", "type": "extraction", "used": ["source:pmid:39228680"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE134431", "type": "accession_resolution", "used": ["data:geo:GSE134431"], "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:39228680", "parent_hash": "sha256:6c106de83cfd842aab969d73bef597c76e7c980d7800e57904461908af824064", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE134431 licence not read", "asset:data:geo:GSE134431 not downloaded", "asset:data:geo:GSE147890 licence not read", "asset:data:geo:GSE147890 not downloaded", "asset:data:geo:GSE23006 licence not read", "asset:data:geo:GSE23006 not downloaded", "asset:data:geo:GSE80178 licence not read", "asset:data:geo:GSE80178 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": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["mice models. Methods: GSE80178 and GSE134431 microarray datasets were fetched from the GEO database,", "nd (Figure 9A). Then, to verify this result, microarray data (GSE23006) was discovered which analyze", "(1d, 3d, 5d, 7d and 10d) were extracted from microarray dataset (GSE23006). n=3 at every different t"], "order_basis": "paper_text_order:550", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["e, 6–8 weeks old) were procured. All C57BL/6 mice were raised under controlled environmental condit", ") with free access to food and water. C57/B6 mice were anaesthetized through intraperitoneal inject", "for external validation. For diabetic model, mice were induced through intraperitoneal injection wi", "to day 10 during skin wound healing process in mice skin wounds (Figure 9B). Our results and ext"], "order_basis": "paper_text_order:3859", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["1; (I) TLR3; (J) TLR4. Validation of CTSH by RT-qPCR and Western Blot in Animal Models The expres", "healing process of wounds in wild-type mice. RT-qPCR results showed dynamic changes in CTSH expre", "pe or diabetic mice on day 5 was analyzed by RT-qPCR and Western blot. Our results showed that CT", "(Figure 9C-D).Figure 9Validation of CTSH by RT-qPCR and Western blot in animal models. (A) The e"], "order_basis": "paper_text_order:9566", "id": "assay:3"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["(J) TLR4. Validation of CTSH by RT-qPCR and Western Blot in Animal Models The expression of CTSH was", "ic mice on day 5 was analyzed by RT-qPCR and Western blot. Our results showed that CTSH expression was", "D).Figure 9Validation of CTSH by RT-qPCR and Western blot in animal models. (A) The expression of CTSH", "e on day 5 was analyzed by RT-qPCR (n=5) and Western blot (n=4). All results are expressed as means ±"], "order_basis": "paper_text_order:9579", "id": "assay:4"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Hub genes were analyzed using three machine learning methods (LASSO, RF and SVM-RFE)."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]},"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":"2026-11-04T02:35:43+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}}}