{"id":"W4296471666","doi":"10.1093/jamia/ocac143","title":"Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy","year":2022,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital; Université de Montréal","funders":"National Center for Advancing Translational Sciences; National Eye Institute; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Healthcare delivery; Health care; Clinical decision support system; Decision support system; Computer science; Personalized medicine; Clinical decision making; Health care delivery; Medicine; Artificial intelligence; Intensive care medicine; Bioinformatics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02482515,0.000234665,0.001374528,0.0002048982,0.001427269,0.00003340161,0.001015247,0.0003320882,0.0003158732],"category_scores_gemma":[0.003254694,0.0001664267,0.0006662901,0.0005332155,0.0001680457,0.0002641143,0.0005370387,0.005362929,0.00005136734],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003712657,"about_ca_system_score_gemma":0.007284134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005557374,"about_ca_topic_score_gemma":0.0000985001,"domain_scores_codex":[0.9834903,0.006374401,0.005453497,0.0001638292,0.003585685,0.0009323297],"domain_scores_gemma":[0.9792287,0.00760814,0.01118609,0.0003728644,0.0008796074,0.0007245556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007457385,0.0003545992,0.5146202,0.0005536357,0.0005949295,0.00004257635,0.01976297,0.0003170183,0.000001019089,0.0002805457,0.2096037,0.253123],"study_design_scores_gemma":[0.01670943,0.01024091,0.1818377,0.001827005,0.0004504783,0.0005501097,0.3891928,0.1685312,0.000002254356,0.0005338431,0.2291164,0.00100779],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677901,0.0003607551,0.003247592,0.01618907,0.01117579,0.000805826,0.00003938578,0.00007475615,0.0003167242],"genre_scores_gemma":[0.9625918,0.001911224,0.002343666,0.03008284,0.002706844,0.00004217037,0.0000219223,0.0000439037,0.0002556669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3694298,"threshold_uncertainty_score":0.9998727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08028676825393923,"score_gpt":0.470961002110491,"score_spread":0.3906742338565518,"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."}}