{"id":"W7095469748","doi":"","title":"The Structural Influences of Patients in French, Canadian and American Hospitals","year":2007,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"MEDLINE; Government (linguistics); Health care; Public health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001289973,0.00004840097,0.00004974479,0.00002909972,0.00004007328,0.000009041043,0.00008959475,0.00002690912,0.000003335264],"category_scores_gemma":[0.0001115655,0.00003153801,0.00001027393,0.00005845744,0.0001435584,0.00000174596,0.00002995397,0.00004199001,3.986445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006595358,"about_ca_system_score_gemma":0.00002953561,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07022357,"about_ca_topic_score_gemma":0.3573869,"domain_scores_codex":[0.9995962,0.00001002109,0.0001414997,0.00005728497,0.00005403223,0.0001409306],"domain_scores_gemma":[0.9997444,0.00001464794,0.00006049575,0.0000966263,0.00003168658,0.00005213106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003254704,0.000001042048,0.9836948,0.000002345678,0.000004194357,1.557404e-7,0.00008937759,0.000009368741,0.0001675188,0.00004089168,0.0000896586,0.01589741],"study_design_scores_gemma":[0.00008664627,0.00009235609,0.9958338,0.000001509891,9.351106e-7,5.195958e-7,0.0001593553,0.00006394608,0.00100577,0.0000252142,0.002682929,0.00004698536],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974765,0.00004009583,0.00001025972,0.00005819187,0.00002832816,0.00005774232,0.000004379417,0.000001282386,0.002323263],"genre_scores_gemma":[0.9991183,0.00002270378,0.0006564889,0.0001096294,0.000009496979,6.145734e-7,0.0000132166,0.000002499841,0.00006704738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2871633,"threshold_uncertainty_score":0.9359679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001726503922595811,"score_gpt":0.2281414870643577,"score_spread":0.2264149831417619,"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."}}