{"id":"W4200044365","doi":"10.1016/j.biosystems.2021.104585","title":"AI in predicting COPD in the Canadian population","year":2021,"lang":"en","type":"article","venue":"Biosystems","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Queen's University","funders":"Canadian Institute for Military and Veteran Health Research; Queen's University; International Business Machines Corporation","keywords":"COPD; Medicine; Population; Medical record; Health care; Primary care; Disease; Psychological intervention; Intensive care medicine; Physical therapy; Family medicine; Internal medicine; Nursing","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003433749,0.00005314951,0.00007996515,0.0001054257,0.00009211896,0.0001294614,0.0000554989,0.00005730455,0.000009739097],"category_scores_gemma":[0.00006735323,0.00004599912,0.00001799225,0.0003219322,0.000005512331,0.0001109169,0.000003094416,0.00008984583,0.0000117491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004074163,"about_ca_system_score_gemma":0.00003617282,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5326467,"about_ca_topic_score_gemma":0.9088496,"domain_scores_codex":[0.9991913,0.0001758236,0.0001962116,0.0001295466,0.0001405052,0.0001666373],"domain_scores_gemma":[0.9997388,0.00004823205,0.00003840384,0.0001194146,0.0000231829,0.00003194368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000566554,0.00003117033,0.9899107,0.00002767537,0.000001453947,0.00001974936,0.002546667,0.00004475229,0.0004005048,0.0003039764,0.003744588,0.002963106],"study_design_scores_gemma":[0.0003156022,0.000020001,0.989738,0.0002901785,0.000004802763,0.0000117407,0.0015957,0.0009472414,0.0006039389,0.0001499611,0.006251939,0.00007083915],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873962,0.0002564914,7.889885e-7,0.009231884,0.001562719,0.0002147916,0.000004120504,0.00001381119,0.001319184],"genre_scores_gemma":[0.9984299,9.967864e-7,0.00001674265,0.001301474,0.0001472842,0.00001736578,0.00006157878,0.000007048673,0.00001759276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3762029,"threshold_uncertainty_score":0.4704654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582851956437412,"score_gpt":0.2932613628588016,"score_spread":0.2774328432944275,"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."}}