{"id":"W4294242912","doi":"10.23889/ijpds.v7i3.1892","title":"Integrating administrative and clinical datasets to improve patient outcomes.","year":2022,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"St Joseph's Health Care; London Health Sciences Centre; Western University","funders":"","keywords":"Health care; Process (computing); Medical emergency; Electronic health record; Medical record; Acute care; Data sharing; Business; Medicine; Computer science; Alternative medicine","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004067136,0.0001086337,0.0002021609,0.0002609283,0.002495129,0.0001010715,0.001532919,0.00003325389,0.000244785],"category_scores_gemma":[0.003423289,0.00008976173,0.0000439424,0.0002101276,0.00007579975,0.001256807,0.002236225,0.0006669314,0.0000120821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007232966,"about_ca_system_score_gemma":0.001500283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003801966,"about_ca_topic_score_gemma":0.0003576091,"domain_scores_codex":[0.99696,0.0002249981,0.001035981,0.000468698,0.000967322,0.0003429802],"domain_scores_gemma":[0.9974807,0.0007845763,0.0005839868,0.0004294279,0.0004146326,0.0003066818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003464957,0.000163461,0.6918268,0.0000151025,0.00006623772,0.00002087751,0.001073882,0.00002616572,0.0001138794,0.01218212,0.1371255,0.1570395],"study_design_scores_gemma":[0.001232915,0.000631551,0.4064641,0.00003130047,0.00002170117,0.00004487277,0.003051739,0.002757531,0.000004092335,0.001743224,0.5837876,0.000229393],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8125673,0.00008452826,0.01126059,0.09012896,0.04823562,0.003139798,0.03265717,0.0001001812,0.001825896],"genre_scores_gemma":[0.9395077,0.00002656101,0.01867582,0.03795042,0.0006130572,0.0001157181,0.002728529,0.00001779828,0.0003644428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4466621,"threshold_uncertainty_score":0.9988035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2293786824299048,"score_gpt":0.5947366517715013,"score_spread":0.3653579693415964,"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."}}