{"id":"W1972578955","doi":"10.1177/1545968313520413","title":"Identifying Homogeneous Subgroups in Neurological Disorders","year":2014,"lang":"en","type":"article","venue":"Neurorehabilitation and neural repair","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; International Collaboration On Repair Discoveries; Vancouver Coastal Health","funders":"","keywords":"Recursive partitioning; Clinical trial; Logistic regression; Confidence interval; Homogeneous; Psychological intervention; Physical medicine and rehabilitation; Medicine; Computer science; Machine learning; Mathematics; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009255473,0.0005452234,0.0006880183,0.002755012,0.0004241968,0.0008539073,0.0006597614,0.0006870974,0.001033334],"category_scores_gemma":[0.03754848,0.0001490752,0.0006961823,0.001056114,0.0006859158,0.0008982641,0.001252985,0.0005489207,0.0002898838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004199348,"about_ca_system_score_gemma":0.0009621754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002063883,"about_ca_topic_score_gemma":0.001877767,"domain_scores_codex":[0.9951652,0.003157347,0.0003328559,0.0006930652,0.0004385179,0.0002129778],"domain_scores_gemma":[0.9757268,0.01740957,0.003343955,0.001870676,0.001259752,0.0003892877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001416866,0.0001699438,0.7217139,0.0003373722,0.000631901,0.0003498672,0.001014887,0.01741763,0.003610688,0.00641298,0.001690845,0.2452331],"study_design_scores_gemma":[0.0002358492,0.001810342,0.7235776,0.0004165712,0.0008855591,0.001456399,0.001453,0.1515685,0.008591687,0.1014612,0.008388315,0.0001550099],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8002325,0.003240805,0.1911775,0.0007087141,0.00008084477,0.0007599101,0.001229695,0.0003449107,0.00222521],"genre_scores_gemma":[0.9741861,0.0002344632,0.02433404,0.0001339879,0.00007311764,0.0002390372,0.0006158527,0.00002671768,0.000156633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009255473,"threshold_uncertainty_score":0.04894817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392995950144759,"score_gpt":0.2732679337258805,"score_spread":0.2593379742244329,"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."}}