{"id":"W2077323069","doi":"10.1097/jom.0b013e3181954e3e","title":"Trends in Components of Medical Spending Within Workers Compensation: Results From 37 States Combined","year":2009,"lang":"en","type":"article","venue":"Journal of Occupational and Environmental Medicine","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Occupational Safety and Health","keywords":"Workers' compensation; Compensation (psychology); Medical costs; Quarter (Canadian coin); Medical insurance; Medical services; Actuarial science; Medicine; Medical diagnosis; Business; Health insurance; Demographic economics; Environmental health; Health care; Economics; Psychology; Geography; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007184463,0.0002677888,0.0003398145,0.002020429,0.000272924,0.0006282326,0.0002652601,0.0002805069,0.001172124],"category_scores_gemma":[0.001754781,0.0002329796,0.0007657267,0.003279532,0.0001711126,0.0004184866,0.0007199819,0.0003719229,0.0002164268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009131195,"about_ca_system_score_gemma":0.0006508289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06113764,"about_ca_topic_score_gemma":0.08159971,"domain_scores_codex":[0.9994239,0.000104453,0.00008401868,0.000125985,0.0001410618,0.0001206241],"domain_scores_gemma":[0.9979135,0.0002768291,0.001141482,0.0001112043,0.0003857174,0.0001712762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007283488,0.00001758705,0.9976431,0.00001105756,0.0001129615,0.00002643148,0.00007515723,0.0002364615,0.00006320188,0.00001193085,0.0002026456,0.00152657],"study_design_scores_gemma":[0.000003045709,0.00002598531,0.9992989,0.000005304101,0.0000526258,0.00003662707,0.0001479411,0.0001914337,0.00005502171,0.000006537789,0.0001742623,0.000002266206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962054,0.0001407206,0.00004624627,0.0000328461,0.000001711485,0.000005635013,0.00328432,0.000005134179,0.0002779955],"genre_scores_gemma":[0.9950322,0.0001544359,0.00006384794,0.00001823768,0.0000046133,0.0000131392,0.00446426,0.00000374999,0.0002455387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06113764,"threshold_uncertainty_score":0.1215636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07966450120179243,"score_gpt":0.3065530399782581,"score_spread":0.2268885387764657,"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."}}