{"id":"W2114462353","doi":"10.12927/hcq.2009.20680","title":"SAS Health Insight and Prediction Platform: Ontario Ministry of Health and Long-Term Care Forecast for Hip Replacements","year":2009,"lang":"en","type":"article","venue":"Healthcare Quarterly","topic":"Hip and Femur Fractures","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Orthopedic surgery; Christian ministry; Health care; Medicine; Term (time); Health services; Service (business); Population ageing; Operations management; Population; Business; Environmental health; Surgery; Engineering; Economic growth; Marketing; Political science","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":[0.001109943,0.0009471948,0.0005903758,0.001766455,0.0006572868,0.001308904,0.001336952,0.0005979112,0.01374798],"category_scores_gemma":[0.007636325,0.0005445337,0.0007155539,0.003860823,0.0001925594,0.0007054459,0.0005889225,0.0007492643,0.006572881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009581162,"about_ca_system_score_gemma":0.01824236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9707973,"about_ca_topic_score_gemma":0.9615819,"domain_scores_codex":[0.9994352,0.00004667892,0.00004143339,0.00007794907,0.0003185669,0.00008000827],"domain_scores_gemma":[0.9961093,0.000283863,0.0003225674,0.0001824159,0.002804975,0.0002968703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002413528,0.00004585218,0.107935,0.0002166725,0.0001173721,0.00006960401,0.0001274009,0.04100299,0.000263397,0.001496114,0.8025016,0.04598267],"study_design_scores_gemma":[0.0003677284,0.00006447962,0.2666716,0.0002374255,0.0001574147,0.00009493089,0.0003604145,0.3967856,0.0010131,0.002965609,0.3311272,0.0001544477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02614018,0.000518272,0.01058381,0.002845195,0.0002392206,0.0002632335,0.930801,0.007823089,0.020786],"genre_scores_gemma":[0.2149602,0.001170321,0.01947644,0.000305317,0.0002206413,0.0002530464,0.7428702,0.001040573,0.01970318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0292027,"threshold_uncertainty_score":0.06951654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03374273268684803,"score_gpt":0.3263697677996378,"score_spread":0.2926270351127898,"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."}}