{"id":"W2915005312","doi":"10.2196/10773","title":"A Computer Application to Predict Adverse Events in the Short-Term Evolution of Patients With Exacerbation of Chronic Obstructive Pulmonary Disease","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Red de Investigación en Servicios de Salud en Enfermedades Crónicas; Agencia Laín Entralgo; Ministerio de Economía y Competitividad; Instituto de Salud Carlos III; Comunidad de Madrid; Ministerio de Sanidad, Servicios Sociales e Igualdad; Eusko Jaurlaritza; Basque Center for Applied Mathematics; Euskal Herriko Unibertsitatea","keywords":"Exacerbation; Medicine; Pulmonary disease; Intensive care medicine; Term (time); Disease; Pulmonary function testing; Adverse effect; Cardiology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001007838,0.0008516659,0.0004422758,0.001446426,0.0001628444,0.0005754678,0.0005763901,0.0005319955,0.005783541],"category_scores_gemma":[0.005739507,0.0002762385,0.0004809216,0.0005660718,0.0001452459,0.0005346916,0.0006911232,0.0004057441,0.0015772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002099463,"about_ca_system_score_gemma":0.0004629199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210466,"about_ca_topic_score_gemma":0.001353845,"domain_scores_codex":[0.9994918,0.0001538723,0.00007730105,0.0001426495,0.00009681254,0.00003741094],"domain_scores_gemma":[0.9949885,0.004014811,0.0003004636,0.0001422647,0.0003944825,0.0001594974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004640318,0.001378583,0.140461,0.001830278,0.000418975,0.002381589,0.001092827,0.01120471,0.01266079,0.001251226,0.08881664,0.7338632],"study_design_scores_gemma":[0.002599625,0.00402206,0.4113352,0.002110461,0.001343776,0.01062709,0.0008371293,0.3943639,0.02874934,0.006611359,0.1367778,0.0006223328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.615712,0.003172494,0.1727153,0.002390497,0.0006406016,0.004625591,0.02603148,0.1542448,0.02046723],"genre_scores_gemma":[0.7994188,0.001434712,0.1746745,0.001068093,0.0002410279,0.002061712,0.01219597,0.001132996,0.007772143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005783541,"threshold_uncertainty_score":0.01934785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006615819724941587,"score_gpt":0.2656350147386466,"score_spread":0.259019195013705,"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."}}