{"id":"W2993629479","doi":"10.23889/ijpds.v4i1.1116","title":"Achieving cross provincial comparisons of osteoporosis screening performance from administrative health data.","year":2019,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Waterloo; Western University","funders":"","keywords":"Operationalization; Identification (biology); Flexibility (engineering); Health care; Work (physics); Computer science; Political science; Engineering; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.003653707,0.00009959668,0.0002414595,0.0001755214,0.0002583625,0.0002107535,0.001570898,0.00003136484,0.0001195261],"category_scores_gemma":[0.004204324,0.00008988737,0.00004053906,0.0002068982,0.0001289919,0.004595698,0.0006447171,0.0002416761,0.000008893254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001581181,"about_ca_system_score_gemma":0.00101746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001326446,"about_ca_topic_score_gemma":0.000315356,"domain_scores_codex":[0.9967406,0.00004595872,0.001292845,0.0004583667,0.001261639,0.0002006289],"domain_scores_gemma":[0.9963431,0.0005048623,0.001395079,0.0007129312,0.0008933716,0.0001506208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005931181,0.0001082651,0.9391229,0.0000201746,0.00005089473,9.892751e-7,0.0001241391,0.0004150082,0.00127284,0.0001061936,0.001310999,0.05687454],"study_design_scores_gemma":[0.001646923,0.000325332,0.8583198,0.0001992723,0.00003354052,0.000048833,0.0004317029,0.133837,0.0001644605,0.00002806811,0.004874714,0.00009032711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588085,0.00002849772,0.03254287,0.003578583,0.001668429,0.0004905481,0.002825285,0.00001346841,0.00004375015],"genre_scores_gemma":[0.9070499,0.0000282403,0.08556597,0.0004523164,0.0004846646,0.000001868977,0.006373359,0.000008820939,0.00003486261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.133422,"threshold_uncertainty_score":0.5033272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5093538756730761,"score_gpt":0.6014346457643377,"score_spread":0.09208077009126159,"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."}}