{"id":"W4321443669","doi":"10.2196/46366","title":"Correction: Personalized Precision Medicine for Health Care Professionals: Development of a Competency Framework","year":2023,"lang":"en","type":"erratum","venue":"JMIR Medical Education","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Personalized medicine; Precision medicine; Health professionals; Health care; Medicine; Medical education; Computer science; Bioinformatics; Political science; Pathology; Biology","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":[0.009406358,0.002756622,0.002179453,0.003504263,0.004812723,0.005971017,0.004918831,0.01736801,0.05336495],"category_scores_gemma":[0.1419618,0.001585481,0.002512017,0.002575722,0.004867863,0.003327611,0.003228851,0.02286103,0.02929876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006480725,"about_ca_system_score_gemma":0.01436294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03234687,"about_ca_topic_score_gemma":0.03889972,"domain_scores_codex":[0.9878243,0.002341922,0.002155129,0.001172292,0.005493452,0.00101299],"domain_scores_gemma":[0.9269434,0.02610536,0.003277765,0.002947198,0.03785903,0.002867243],"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":[0.00002123701,0.000004931212,0.00005303642,0.0001021428,0.000009604189,0.0001968757,0.00003653293,0.00002869883,0.00001736376,0.0008928021,0.9954502,0.003186536],"study_design_scores_gemma":[0.00008893188,0.00002587224,0.0005353888,0.001054766,0.00006064032,0.0009811185,0.0001938933,0.0004028577,0.000257006,0.00312565,0.9932042,0.00006959352],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0000947287,0.0006837086,0.0008167597,0.1403512,0.8541507,0.00004281451,0.0009685465,0.0003402361,0.002551403],"genre_scores_gemma":[0.01227415,0.009555889,0.008025507,0.308255,0.5232916,0.0004774893,0.001990239,0.001410086,0.13472],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.05336495,"threshold_uncertainty_score":0.1785236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09315100074171703,"score_gpt":0.5358064219861518,"score_spread":0.4426554212444348,"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."}}