{"id":"W2974267758","doi":"10.1038/s41591-019-0609-x","title":"Author Correction: Do no harm: a roadmap for responsible machine learning for health care","year":2019,"lang":"en","type":"erratum","venue":"Nature Medicine","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Brain Institute; SickKids Foundation; Vector Institute; University of Toronto","funders":"","keywords":"Harm; Do no harm; Health care; Political science; Medicine; Engineering ethics; Psychology; Engineering; Law; Psychiatry","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":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.01130443,0.0007866244,0.002692854,0.0009080348,0.000548982,0.00004717289,0.0007030464,0.006957567,0.0008117397],"category_scores_gemma":[0.1745038,0.000588274,0.0006355488,0.000751099,0.0004257478,0.00005905801,0.0002238334,0.03326555,0.0001202275],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204804,"about_ca_system_score_gemma":0.009934797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001651063,"about_ca_topic_score_gemma":0.00053003,"domain_scores_codex":[0.9920643,0.0004208148,0.001503931,0.001740451,0.003000131,0.00127036],"domain_scores_gemma":[0.9725989,0.01849651,0.0008759,0.00152065,0.005632936,0.0008751741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.006233888,0.00009203979,0.0001148983,0.01324715,0.0002728813,0.00002663081,0.00109922,0.000003082278,0.00005542576,0.0008187592,0.970397,0.007639023],"study_design_scores_gemma":[0.006648222,0.01228174,0.0001494154,0.01183108,0.0003853128,0.00006269968,0.0006701755,0.001259377,0.00004925366,0.0007515238,0.965481,0.000430186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.00002610573,0.2163606,0.002268818,0.3997096,0.2891287,0.01321571,0.0006184088,0.0005588114,0.0781133],"genre_scores_gemma":[0.0009022751,0.004697178,0.003824848,0.01539586,0.02947154,0.0005125694,0.008266816,0.0003481825,0.9365807],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8584675,"threshold_uncertainty_score":0.9996569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1929116728470182,"score_gpt":0.5817956483190083,"score_spread":0.3888839754719902,"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."}}