{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007005383,0.0001237076,0.0003549455,0.0003563879,0.00004720415,0.000009312443,0.0001417409,0.00006361518,0.001224369],"category_scores_gemma":[0.0001046302,0.0000889287,0.0003993017,0.001200114,0.00007358551,0.0002123367,0.00006742956,0.0001156924,0.00006893113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004924828,"about_ca_system_score_gemma":0.00003294861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002337108,"about_ca_topic_score_gemma":0.00001759806,"domain_scores_codex":[0.9986979,0.0001785017,0.0003576567,0.0001789572,0.0003501939,0.0002368196],"domain_scores_gemma":[0.9988528,0.00004503749,0.0002187073,0.0006778928,0.00008489375,0.0001205926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006934183,0.001027611,0.6805869,0.0000877801,0.003465086,0.000001929187,0.0003478345,0.000008261033,0.01419642,0.0007994339,0.01875647,0.2800289],"study_design_scores_gemma":[0.0003062717,0.0001547464,0.9716135,0.00003914753,0.003114242,0.000002048825,0.00005855952,0.0002025601,0.003167152,0.00008985817,0.02113538,0.000116513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99372,0.0005317189,0.0003495157,0.0003397377,0.0002428203,0.0002112807,0.0003582441,0.00007856795,0.004168124],"genre_scores_gemma":[0.9957289,0.00004270333,0.0001532383,0.0001360596,0.0001667715,0.00002700783,0.0004266375,0.00001473199,0.003304029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2910267,"threshold_uncertainty_score":0.9996886,"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."}}