{"id":"W3134788386","doi":"10.12927/hcpol.2021.26433","title":"Estimating Population Benefits of Prevention Approaches Using a Risk Tool: High Resource Users in Ontario, Canada","year":2021,"lang":"en","type":"article","venue":"Healthcare policy","topic":"Health Promotion and Cardiovascular Prevention","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trillium Health Centre; Public Health Ontario","funders":"","keywords":"Health care; Resource (disambiguation); Population; Actuarial science; Business; Operations management; Environmental health; Computer science; Medicine; Economics; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183121,0.0001176683,0.0003790082,0.00016805,0.0001016983,0.000007647064,0.00003451368,0.0001262817,0.00003886246],"category_scores_gemma":[0.0003549959,0.0001319512,0.0001202445,0.0004567376,0.00001072313,0.00007230593,0.00003077768,0.0003377385,6.238487e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001848659,"about_ca_system_score_gemma":0.004514321,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935242,"about_ca_topic_score_gemma":0.9894425,"domain_scores_codex":[0.9975578,0.0008652163,0.0006083772,0.000273522,0.0003942787,0.0003008162],"domain_scores_gemma":[0.9991459,0.00002848653,0.0002479778,0.0003112248,0.0001214592,0.0001449813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005182537,0.0001974951,0.6052996,0.002381823,0.0001063681,0.00002959224,0.001654424,0.02074607,0.00003237017,0.002245351,0.00003758732,0.3672175],"study_design_scores_gemma":[0.0009562874,0.00006665874,0.9931905,0.0008739726,0.0000534541,0.00007876848,0.0002310842,0.003776704,0.000109416,0.0003693535,0.0001938709,0.00009993106],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956259,0.0003603672,0.001370151,0.001863408,0.0001146401,0.000485556,0.00001664455,0.00002048284,0.0001428856],"genre_scores_gemma":[0.9893005,0.00001768177,0.009762217,0.0003334719,0.0001738828,0.00001592797,0.000210317,0.00002053046,0.0001653975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3878909,"threshold_uncertainty_score":0.8008211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0884615150254139,"score_gpt":0.3321640485621995,"score_spread":0.2437025335367856,"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."}}