{"id":"W4405950205","doi":"10.1038/s44303-024-00063-x","title":"Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers","year":2024,"lang":"en","type":"article","venue":"npj Imaging","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Hospital; Vector Institute; Ontario Brain Institute; Sunnybrook Health Science Centre; University of Toronto; Toronto Metropolitan University; St. Michael's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fluid-attenuated inversion recovery; Medicine; Biomarker; Internal medicine; Disease; Imaging biomarker; Subgroup analysis; Oncology; White matter; Magnetic resonance imaging; Confidence interval; Radiology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000165559,0.0002391216,0.0002355353,0.0002095729,0.0002475676,0.0002054333,0.00005585547,0.00002820665,0.0001126722],"category_scores_gemma":[0.00007648142,0.0002209792,0.0002513937,0.0002248919,0.00009477143,0.0002426923,0.00008911474,0.0002060903,0.00001617735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001131127,"about_ca_system_score_gemma":0.0001057384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001789355,"about_ca_topic_score_gemma":0.000009342451,"domain_scores_codex":[0.9985231,0.00007248449,0.000229954,0.0004948076,0.0003629307,0.0003167159],"domain_scores_gemma":[0.9992685,0.00003829614,0.00003195009,0.0002222615,0.00006932829,0.0003696421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003666516,0.00008348862,0.9553976,0.0007877625,0.0003116984,0.0002409553,0.0003729984,0.00005424021,0.002669855,0.00001930913,0.00004525428,0.03998015],"study_design_scores_gemma":[0.004643361,0.0001771235,0.4565544,0.00280054,0.003578276,0.0003869461,0.00137916,0.5084137,0.001301589,0.0003736891,0.01897467,0.001416571],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781568,0.01882797,0.001633875,0.0002193152,0.0002435494,0.0003615545,0.00001444517,0.0002714871,0.0002709947],"genre_scores_gemma":[0.9982547,0.0003338588,0.0008452657,0.0001519224,0.0001477405,0.00001111038,0.0001487619,0.00006460084,0.00004202362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5083594,"threshold_uncertainty_score":0.9011269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006015041518542001,"score_gpt":0.2478737102884903,"score_spread":0.2418586687699483,"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."}}