{"id":"W4312036768","doi":"10.1093/geroni/igac059.2770","title":"MACHINE LEARNING TO PREDICT HOMEBOUND STATUS IN OLDER ADULTS USING CANADIAN LONGITUDINAL STUDY ON AGING DATASET","year":2022,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute","funders":"","keywords":"Categorical variable; Gerontology; Random forest; Missing data; Medicine; Cohort; Imputation (statistics); Classifier (UML); Population; Machine learning; Artificial intelligence; Computer science; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00233654,0.0008701311,0.0009055025,0.00337053,0.001046118,0.0009287299,0.001803661,0.0007780033,0.001809882],"category_scores_gemma":[0.007938925,0.0002100537,0.001127158,0.003730923,0.0002485576,0.0003640951,0.0007183128,0.001301968,0.0008264486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005353108,"about_ca_system_score_gemma":0.006054864,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8705772,"about_ca_topic_score_gemma":0.8879757,"domain_scores_codex":[0.9991571,0.0001218127,0.00009060265,0.0002327833,0.0002231457,0.0001745474],"domain_scores_gemma":[0.9968694,0.000578776,0.0002078094,0.0003299674,0.001717913,0.0002961094],"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":[0.0008908157,0.0005109023,0.7355924,0.0006114601,0.0009728505,0.0003709497,0.0001909091,0.02072988,0.0005571356,0.0008868258,0.1759114,0.06277438],"study_design_scores_gemma":[0.0002755443,0.0001686625,0.7331747,0.0003381685,0.0003400578,0.0001928048,0.0006532878,0.2295014,0.0007994656,0.00135132,0.03307067,0.000133955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5014024,0.003029936,0.004102373,0.001860671,0.0002835091,0.0003084314,0.4854698,0.0007468155,0.002796088],"genre_scores_gemma":[0.4341785,0.0007461846,0.009444597,0.0003139699,0.00008136915,0.0002501497,0.5533655,0.00004088943,0.001578861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1294228,"threshold_uncertainty_score":0.2603699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06958576020290046,"score_gpt":0.39052320162437,"score_spread":0.3209374414214695,"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."}}