{"id":"W4280553512","doi":"10.1136/thoraxjnl-2021-218563","title":"Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis","year":2022,"lang":"en","type":"article","venue":"Thorax","topic":"Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"NIHR Leicester Biomedical Research Centre; Medical Research Council; British Lung Foundation; National Institute for Health and Care Research; Canadian Institutes of Health Research; U.S. Department of Veterans Affairs; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Medicine; Idiopathic pulmonary fibrosis; Transcriptome; Gene expression profiling; Disease; Pathogenesis; Biomarker; Biomarker discovery; Bioinformatics; Immunology; Gene; Computational biology; Gene expression; Internal medicine; Lung; Proteomics; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004868937,0.0001733713,0.0007317587,0.0006643125,0.0000995282,0.000007606767,0.0002840642,0.00005168874,0.0009956723],"category_scores_gemma":[0.00004148214,0.0001704173,0.0005333163,0.001299381,0.00007509132,0.00008801214,0.0002071529,0.0001929134,0.00001310489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000886097,"about_ca_system_score_gemma":0.0001046849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002979501,"about_ca_topic_score_gemma":0.000009595044,"domain_scores_codex":[0.998055,0.0001772657,0.000588003,0.0003581707,0.0005637131,0.000257874],"domain_scores_gemma":[0.9989047,0.00004370218,0.0001658271,0.0006166932,0.00008569877,0.0001834028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02426792,0.001598884,0.01349706,0.00140699,0.002680218,0.0002479132,0.001396429,0.001626284,0.9409146,0.0005729065,0.007663468,0.004127389],"study_design_scores_gemma":[0.005472184,0.02678486,0.7174103,0.002209926,0.06135271,0.0005067928,0.005950367,0.03234065,0.1009828,0.0007953729,0.04368083,0.002513146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883685,0.002053386,0.0004636273,0.0006793144,0.0002695507,0.000502153,0.006768522,0.00003929533,0.0008556438],"genre_scores_gemma":[0.9978368,0.00003318311,0.0002170277,0.0004351593,0.00006523794,0.0000530304,0.001133685,0.00002530777,0.0002006079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8399317,"threshold_uncertainty_score":0.9999176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159937682958315,"score_gpt":0.3094060180863028,"score_spread":0.2934122497904713,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}