{"id":"W2047515884","doi":"10.1136/injuryprev-2012-040580b.25","title":"ANALYSIS OF INJURY TRENDS—SHOW ME THE ELBOW!","year":2012,"lang":"en","type":"article","venue":"Injury Prevention","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada","funders":"","keywords":"Poison control; Statistical software; Computer science; Statistics; Medicine; Medical emergency; Data science; Mathematics","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":[0.01842035,0.0008311325,0.0009791137,0.005582877,0.000565873,0.002450164,0.00162765,0.0008758858,0.01420346],"category_scores_gemma":[0.08470201,0.0004147702,0.002138474,0.007773155,0.000894781,0.002394019,0.001935543,0.001873073,0.002789153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001406498,"about_ca_system_score_gemma":0.001986941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02411422,"about_ca_topic_score_gemma":0.01763812,"domain_scores_codex":[0.9893771,0.005428052,0.001058027,0.001501299,0.002243591,0.0003918775],"domain_scores_gemma":[0.9557065,0.02291466,0.008699399,0.004542756,0.007325849,0.0008108403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001080908,0.0002184344,0.4303072,0.003811728,0.003264834,0.0003499615,0.002468558,0.006213358,0.001602897,0.01107188,0.1610557,0.3785547],"study_design_scores_gemma":[0.0002788425,0.00190496,0.6789715,0.004054671,0.001349762,0.0008498542,0.005589532,0.03540163,0.004378011,0.03614204,0.2307706,0.0003086298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3312964,0.01506597,0.4164118,0.04574062,0.004448875,0.002745074,0.1446578,0.01005734,0.02957604],"genre_scores_gemma":[0.7265198,0.00415084,0.2109569,0.005269724,0.001732574,0.003028039,0.0376046,0.00201491,0.008722566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02411422,"threshold_uncertainty_score":0.09741729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750776524016818,"score_gpt":0.3365438557911298,"score_spread":0.3190360905509616,"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."}}