{"id":"W4296523277","doi":"10.2196/37833","title":"Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute of Neurological Disorders and Stroke; Agency for Healthcare Research and Quality; Cincinnati Children's Hospital Medical Center","keywords":"Clinical decision support system; Software deployment; Artificial intelligence; Emergency department; Computer science; Health care; Certification; Identification (biology); Machine learning; Decision support system; Medicine; Knowledge management; Nursing; Software engineering","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":[0.05875949,0.001171056,0.0004606164,0.001262376,0.0005394269,0.001755511,0.002469567,0.001286883,0.00295109],"category_scores_gemma":[0.1060609,0.0008244915,0.0008161892,0.0008868982,0.001103363,0.002504646,0.002201498,0.001588528,0.001262111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002462878,"about_ca_system_score_gemma":0.008472059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007259862,"about_ca_topic_score_gemma":0.004486397,"domain_scores_codex":[0.978551,0.0136795,0.001800495,0.001605553,0.003329841,0.001033591],"domain_scores_gemma":[0.9050189,0.05880928,0.003699168,0.01115786,0.01920537,0.002109355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005171454,0.01365466,0.1946815,0.001144257,0.0005787035,0.0005105093,0.004080261,0.04152625,0.0156921,0.003562044,0.01154313,0.7078551],"study_design_scores_gemma":[0.006686045,0.03508231,0.1914114,0.001475065,0.0009680152,0.001196279,0.002563341,0.6253904,0.08790395,0.004376842,0.04260449,0.0003419085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7482638,0.0004928093,0.2179737,0.001409626,0.0002027683,0.01468236,0.001900073,0.009178456,0.005896384],"genre_scores_gemma":[0.6374938,0.0002355311,0.3529721,0.0003431563,0.00003617278,0.004371617,0.002866911,0.0003486067,0.001332052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05875949,"threshold_uncertainty_score":0.3107536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2412855034647876,"score_gpt":0.5724309039196083,"score_spread":0.3311454004548207,"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."}}