{"id":"W2151658938","doi":"10.1177/2150131910397704","title":"The SIMARD Screening Tool to Identify Unfit Drivers","year":2011,"lang":"en","type":"article","venue":"Journal of Primary Care & Community Health","topic":"Older Adults Driving Studies","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; NOSM University; University of Ottawa; Lakehead University","funders":"Canada Research Chairs","keywords":"Cut-off; Indeterminate; Medicine; Cut-point; Receiver operating characteristic; Statistics; Set (abstract data type); Identification (biology); Test (biology); Combinatorics; Artificial intelligence; Mathematics; Computer science; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004703053,0.0009484943,0.0006862257,0.007459432,0.0002872476,0.001079786,0.00063618,0.0006842708,0.003616188],"category_scores_gemma":[0.02163393,0.0002847125,0.0009402336,0.002064527,0.0002857805,0.0006969962,0.001066477,0.0008676399,0.00134995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004491714,"about_ca_system_score_gemma":0.0005265474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002983116,"about_ca_topic_score_gemma":0.00477814,"domain_scores_codex":[0.9973758,0.001143649,0.000231947,0.0002360623,0.0008091824,0.0002032641],"domain_scores_gemma":[0.9890887,0.006845936,0.001587256,0.0004002534,0.001720467,0.000357305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00335213,0.0005511748,0.6456741,0.0005156515,0.0005240524,0.0005929551,0.0009922797,0.004240473,0.003155152,0.003331825,0.04229278,0.2947774],"study_design_scores_gemma":[0.0005097736,0.00504379,0.7800348,0.0006960075,0.0009588265,0.006298187,0.001313765,0.1160809,0.02045974,0.00707266,0.06106136,0.0004701878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8786811,0.003262266,0.06465838,0.002632945,0.0006427752,0.0009878421,0.0114792,0.004325133,0.0333304],"genre_scores_gemma":[0.9186004,0.0006970235,0.071321,0.0006650299,0.0001427834,0.0006521997,0.003055017,0.0001741243,0.00469227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007459432,"threshold_uncertainty_score":0.02487248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1597206929801137,"score_gpt":0.4405590897993624,"score_spread":0.2808383968192487,"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."}}