{"id":"W2938039173","doi":"10.1097/ede.0000000000000990","title":"Would Stronger Seat Belt Laws Reduce Motor Vehicle Crash Deaths?","year":2019,"lang":"en","type":"article","venue":"Epidemiology","topic":"Traffic and Road Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Seat belt; Crash; Frequentist inference; Credible interval; Bayesian probability; Case fatality rate; Confidence interval; Law enforcement; Poison control; Prior probability; Enforcement; Law; Econometrics; Actuarial science; Bayesian inference; Statistics; Business; Economics; Political science; Engineering; Medicine; Computer science; Mathematics; Environmental health; Population","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":[0.0204988,0.0006014159,0.0006734363,0.001332714,0.0003543296,0.002147992,0.001386749,0.00196577,0.007085316],"category_scores_gemma":[0.09980071,0.0003394383,0.001279927,0.00119002,0.002223754,0.00201635,0.0009777895,0.002163658,0.0005372991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002251999,"about_ca_system_score_gemma":0.003032564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01453813,"about_ca_topic_score_gemma":0.009038767,"domain_scores_codex":[0.990447,0.005680413,0.0005173449,0.001545481,0.001172321,0.0006373353],"domain_scores_gemma":[0.8939235,0.080727,0.0188828,0.002531184,0.00289305,0.00104256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001739306,0.0009614274,0.779969,0.00204063,0.003803009,0.0002621687,0.0007358294,0.03731675,0.0005532464,0.03881656,0.00687323,0.1269288],"study_design_scores_gemma":[0.0006615527,0.00144097,0.7895837,0.002282015,0.004144305,0.0002330374,0.001216066,0.05258974,0.002779671,0.1205412,0.02441568,0.0001120797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8643795,0.01240808,0.02816347,0.06188314,0.0004954772,0.0003199493,0.007322486,0.0002731449,0.02475484],"genre_scores_gemma":[0.9936808,0.001414085,0.002419757,0.001503696,0.0001666365,0.00005503621,0.0003810373,0.00001150806,0.0003673009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0204988,"threshold_uncertainty_score":0.1084093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02418984179444994,"score_gpt":0.2627859800931022,"score_spread":0.2385961382986523,"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."}}