{"id":"W3034575096","doi":"10.2196/21379","title":"Correction: Prioritization of Free-Text Clinical Documents: A Novel Use of a Bayesian Classifier","year":2020,"lang":"en","type":"erratum","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Bayesian probability; Prioritization; Artificial intelligence; Classifier (UML); Natural language processing; Naive Bayes classifier; Information retrieval; Machine learning; Data mining; Support vector machine","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":[0.006686913,0.002429498,0.001772482,0.005423825,0.003615977,0.004529156,0.004096214,0.007497292,0.06916495],"category_scores_gemma":[0.1504425,0.001286454,0.00183253,0.003566023,0.003133742,0.002646121,0.002600786,0.009895718,0.03435494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003891052,"about_ca_system_score_gemma":0.007859986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03017519,"about_ca_topic_score_gemma":0.03530379,"domain_scores_codex":[0.9921483,0.001533217,0.001661329,0.0009539062,0.00325681,0.0004463566],"domain_scores_gemma":[0.9209582,0.02940873,0.003179472,0.004263558,0.04003852,0.002151644],"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":[0.00004148515,0.000005191564,0.00009533048,0.000143928,0.00001816131,0.0004588293,0.00004552821,0.0000713707,0.00006538129,0.000757386,0.9913276,0.006969859],"study_design_scores_gemma":[0.00007954428,0.00002519989,0.0007478,0.0005890907,0.00006905861,0.001918801,0.0001254551,0.0009221153,0.0006087737,0.003219638,0.9916276,0.00006697635],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003166109,0.000913174,0.003683167,0.1072383,0.8784054,0.00006688478,0.004465569,0.001210334,0.003700501],"genre_scores_gemma":[0.03438472,0.01019223,0.03928062,0.198657,0.4121405,0.0006805268,0.009848957,0.0063278,0.2884876],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06916495,"threshold_uncertainty_score":0.2313798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03611017026749362,"score_gpt":0.3373994203241242,"score_spread":0.3012892500566305,"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."}}