{"id":"W4294920692","doi":"10.1007/s40520-022-02227-4","title":"Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study","year":2022,"lang":"en","type":"article","venue":"Aging Clinical and Experimental Research","topic":"Older Adults Driving Studies","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Space (punctuation); Computer science; Artificial intelligence; Gerontology; Psychology; Machine learning; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003253023,0.000554699,0.000601669,0.001152885,0.0004683024,0.00100803,0.0006837441,0.000560136,0.002388736],"category_scores_gemma":[0.009536969,0.000177222,0.0009100054,0.001108985,0.000358353,0.000781442,0.0007468192,0.0008532217,0.0004882265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002873682,"about_ca_system_score_gemma":0.0004145559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01237609,"about_ca_topic_score_gemma":0.008503922,"domain_scores_codex":[0.9994429,0.0003273648,0.00004449811,0.00008838187,0.00004216324,0.00005470168],"domain_scores_gemma":[0.9946884,0.002820486,0.0008659982,0.0007683823,0.0004307098,0.0004261127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007892065,0.0002366453,0.99208,0.00001374463,0.0002437931,0.00004222605,0.0001764284,0.001146317,0.0001300812,0.0001444244,0.000406892,0.004590053],"study_design_scores_gemma":[0.00004956924,0.0004182209,0.9768535,0.00001877396,0.0002742175,0.0001044355,0.0006345271,0.02032154,0.0001852441,0.0004057634,0.0007201972,0.00001400185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986889,0.00009368901,0.0003464175,0.00007701166,0.000005015699,0.000009670805,0.0005248523,0.000005947707,0.0002486261],"genre_scores_gemma":[0.9983733,0.00004737925,0.0002356152,0.00001036503,0.00001307409,0.00001405294,0.001025659,0.000005077206,0.0002754379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01237609,"threshold_uncertainty_score":0.02460814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2584551541944448,"score_gpt":0.5717015928511859,"score_spread":0.3132464386567411,"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."}}