{"id":"W4295762088","doi":"10.2196/42533","title":"Correction: Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach","year":2022,"lang":"en","type":"erratum","venue":"JMIR Medical Informatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Electronic health record; Health records; Computer science; Unstructured data; Natural language processing; Health data; Natural history; Data science; Data mining; Medicine; Big data; Health care","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":[0.007052333,0.002537336,0.001954831,0.00392997,0.003720952,0.003678811,0.003630858,0.007265347,0.06841353],"category_scores_gemma":[0.1259006,0.00141583,0.001770026,0.003242302,0.002729872,0.002136599,0.002486028,0.01085469,0.02540124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003518288,"about_ca_system_score_gemma":0.008187728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04258898,"about_ca_topic_score_gemma":0.0512286,"domain_scores_codex":[0.992322,0.00176541,0.001757165,0.0008212007,0.002785132,0.0005491519],"domain_scores_gemma":[0.936911,0.02505731,0.002855932,0.00341518,0.02961381,0.00214684],"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":[0.00002710372,0.000005204486,0.0001214642,0.0001044788,0.00001227507,0.0003651872,0.00004410762,0.00004027732,0.00002716337,0.0005563111,0.9951246,0.003571735],"study_design_scores_gemma":[0.0001435329,0.00003466124,0.001773096,0.001005803,0.00009201476,0.002597057,0.0002694851,0.0007894336,0.0004036798,0.003205907,0.9895959,0.00008941087],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003554835,0.0006285524,0.002083562,0.1181236,0.8673756,0.0000587553,0.00658712,0.001012815,0.003774587],"genre_scores_gemma":[0.03872669,0.008731861,0.02796989,0.2551368,0.3881533,0.0008045264,0.01629389,0.005102365,0.2590807],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06841353,"threshold_uncertainty_score":0.228866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02000178949457014,"score_gpt":0.3055084025284567,"score_spread":0.2855066130338865,"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."}}