{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001411437,0.0002903969,0.0004781095,0.002617711,0.002875906,0.002585313,0.001358919,0.001074717,0.009597442],"category_scores_gemma":[0.01401884,0.0003172005,0.0008368698,0.005524319,0.001444165,0.0007516615,0.001472577,0.001519982,0.0005418326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01883675,"about_ca_system_score_gemma":0.01642962,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8003939,"about_ca_topic_score_gemma":0.8793854,"domain_scores_codex":[0.996263,0.0008416223,0.0001861764,0.0004345529,0.0006543793,0.001620181],"domain_scores_gemma":[0.98335,0.004017068,0.003797209,0.0005110251,0.003009061,0.005315645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001230622,0.00002747035,0.9953551,0.000007250527,0.00006816955,0.00008593,0.0004424411,0.0003264259,0.00006713823,0.0006180968,0.001115083,0.001763834],"study_design_scores_gemma":[0.000006394069,0.00002831557,0.9961703,0.00001738891,0.00003192154,0.0001202091,0.002183421,0.0003844848,0.00004983746,0.0002238427,0.0007693172,0.00001464163],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877828,0.0005657874,0.0001724676,0.002925646,0.00002663768,0.00001112828,0.00349498,0.00001572082,0.005004891],"genre_scores_gemma":[0.9983954,0.0001458729,0.00005662849,0.0001414494,0.00001841095,0.00000362896,0.0007625445,0.000008052803,0.0004680206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1996061,"threshold_uncertainty_score":0.4015632,"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."}}