{"id":"W4200348404","doi":"10.1111/rssc.12533","title":"Ranking Tailoring Variables for Constructing Individualized Treatment Rules: An Application to Schizophrenia","year":2021,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Environmental Health Sciences; National Institute of Mental Health; National Institutes of Health; University of Washington","keywords":"Schizophrenia (object-oriented programming); Ranking (information retrieval); Matching (statistics); Intervention (counseling); Antipsychotic; Medicine; Selection (genetic algorithm); Computer science; Psychiatry; Intensive care medicine; Machine learning","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":[0.03763595,0.001088557,0.002920196,0.002424285,0.001265141,0.001932821,0.001680147,0.002030962,0.003417286],"category_scores_gemma":[0.1227234,0.0006792736,0.002011651,0.002936919,0.002038986,0.001922289,0.00269455,0.00388351,0.0002824264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001775484,"about_ca_system_score_gemma":0.003659069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01089396,"about_ca_topic_score_gemma":0.01348422,"domain_scores_codex":[0.9774883,0.0200864,0.0005060496,0.0008526047,0.0007926663,0.0002739254],"domain_scores_gemma":[0.8631535,0.1281322,0.003539933,0.002585213,0.001843421,0.0007458206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001266961,0.000532543,0.01626686,0.0004414705,0.0007382008,0.0004262971,0.000829328,0.5120113,0.0008823383,0.1372331,0.003060813,0.3263108],"study_design_scores_gemma":[0.0004380779,0.0003509642,0.001852232,0.00009724328,0.0001592678,0.0000846144,0.0001513614,0.7849966,0.0005327197,0.2093564,0.001911982,0.00006858873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05899483,0.001391606,0.9354948,0.001926855,0.00007175354,0.0004068594,0.0002287024,0.0002508924,0.001233668],"genre_scores_gemma":[0.4275911,0.001029212,0.5687619,0.0005313786,0.0001181184,0.000512972,0.0002156812,0.00008377784,0.001155847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03763595,"threshold_uncertainty_score":0.1990403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05566241879820685,"score_gpt":0.3588908532884829,"score_spread":0.303228434490276,"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."}}