{"id":"W2807552425","doi":"10.1016/j.hjdsi.2018.05.003","title":"Saving without compromising: Teaching trainees to safely provide high value care","year":2018,"lang":"en","type":"article","venue":"Healthcare","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Value (mathematics); Patient care; Computer science; Medical emergency; Medicine; Medical education; Nursing; Machine learning","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":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00475,0.0005798619,0.0009811778,0.0003917601,0.004789771,0.00007830335,0.0007291604,0.0006649892,0.0002390978],"category_scores_gemma":[0.004201542,0.0005573875,0.0001298637,0.0005398974,0.0001895504,0.0005852364,0.0004398154,0.003319072,0.001485951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00181913,"about_ca_system_score_gemma":0.002948568,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04525153,"about_ca_topic_score_gemma":0.02499884,"domain_scores_codex":[0.9866071,0.007112642,0.001812688,0.001272202,0.001021845,0.002173507],"domain_scores_gemma":[0.9930927,0.001710103,0.000789828,0.001401022,0.001497119,0.001509196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008162293,0.0001776446,0.5194753,0.009799812,0.00008171181,0.00005190794,0.3266256,0.00001102608,0.001747388,0.04768179,0.007669954,0.08586165],"study_design_scores_gemma":[0.002644439,0.002285197,0.4172563,0.006515699,0.00009057754,0.00003194888,0.1347156,0.0001961219,0.0003351463,0.002032163,0.4322562,0.001640669],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8879768,0.000673435,0.000853419,0.09447999,0.003233313,0.004422673,0.0001621659,0.0009238289,0.007274338],"genre_scores_gemma":[0.9269539,0.00002573949,0.01762379,0.04962176,0.003933308,0.0003972145,0.00008928215,0.0001853333,0.001169746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4245862,"threshold_uncertainty_score":0.9996878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5135722074145752,"score_gpt":0.561633849878095,"score_spread":0.04806164246351974,"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."}}