{"id":"W2531904844","doi":"10.1093/intqhc/mzw104.50","title":"ISQUA16-1878ARE STATISTICAL NATURAL LANGUAGE PROCESSING MODELS FOR PNEUMONIA SURVEILLANCE GENERALIZABLE ACROSS ACUTE CARE HOSPITALS?","year":2016,"lang":"en","type":"article","venue":"International Journal for Quality in Health Care","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; McGill University; Université de Sherbrooke","funders":"","keywords":"Generalizability theory; Benchmarking; Pneumonia; Medicine; Health care; Acute care; Health records; Event (particle physics); Natural language processing; Medical emergency; Computer science; Data science; Intensive care medicine; Artificial intelligence; Statistics; Internal medicine; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03420272,0.001108813,0.001080229,0.001622266,0.00116719,0.004674595,0.002683126,0.002564626,0.007724496],"category_scores_gemma":[0.1243504,0.0008529855,0.002249363,0.002114843,0.0015931,0.004516897,0.001382031,0.002078717,0.002827732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01120163,"about_ca_system_score_gemma":0.01443626,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5178759,"about_ca_topic_score_gemma":0.52123,"domain_scores_codex":[0.9828757,0.01081255,0.001092714,0.00279135,0.001800022,0.0006276821],"domain_scores_gemma":[0.8933979,0.06717531,0.005556833,0.01067802,0.02198436,0.001207614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001476045,0.0007609103,0.4195745,0.001439199,0.002573021,0.0002436212,0.001467349,0.1576346,0.002548508,0.01488229,0.05779736,0.3396026],"study_design_scores_gemma":[0.0005777963,0.0005514748,0.1027631,0.0006177807,0.0004603396,0.0001766899,0.0009480864,0.8376288,0.002394077,0.01804855,0.03564967,0.0001837481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.609538,0.01051578,0.2600768,0.05888941,0.001246172,0.001316999,0.03100934,0.004537271,0.02287032],"genre_scores_gemma":[0.8985669,0.001531954,0.06985101,0.004111783,0.0002872108,0.0005573087,0.02100861,0.0004255803,0.003659629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5178759,"threshold_uncertainty_score":0.969927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02831816037239688,"score_gpt":0.4501689080674503,"score_spread":0.4218507476950534,"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."}}