{"id":"W4401021103","doi":"10.1016/j.jogc.2024.102615","title":"Corrigendum to Database Autopsy: An Efficient and Effective Confidential Enquiry into Maternal Deaths in Canada Journal of Obstetrics and Gynaecology Canada (JOGC). Volume 43, Issue 1 (2021) 58–66","year":2024,"lang":"en","type":"erratum","venue":"Journal of Obstetrics and Gynaecology Canada","topic":"Autopsy Techniques and Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Society of Obstetricians and Gynaecologists of Canada; Children's & Women's Health Centre of British Columbia; Izaak Walton Killam Health Centre; BC Children's Hospital; Dalhousie University; Public Health Agency of Canada; University of British Columbia","funders":"","keywords":"Medicine; Obstetrics and gynaecology; Confidentiality; Obstetrics; Autopsy; Family medicine; Gynecology; Pregnancy; Internal medicine; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009399205,0.0007606595,0.002496731,0.001625937,0.000196735,0.0001209187,0.0005387126,0.0007065323,0.0009781016],"category_scores_gemma":[0.007845725,0.0006608584,0.0001340594,0.0009438515,0.000190608,0.0001421155,0.0004190565,0.00327627,5.018663e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.01582826,"about_ca_system_score_gemma":0.05497234,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9887084,"about_ca_topic_score_gemma":0.9970778,"domain_scores_codex":[0.9944919,0.0003185405,0.002146602,0.0006778411,0.001422938,0.0009421275],"domain_scores_gemma":[0.9928218,0.00241375,0.001650437,0.0004220948,0.001190644,0.001501296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00006955608,0.0001567082,0.1686219,0.001421962,0.0009160198,0.1368794,0.0005443241,0.00009989023,0.00001592479,0.00006472129,0.5992467,0.09196301],"study_design_scores_gemma":[0.003600403,0.0021732,0.5332224,0.0002148999,0.001059099,0.002681292,0.001302732,0.001072763,0.00005408683,0.00004366512,0.453811,0.0007645225],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5818532,0.04873626,0.000124514,0.001149524,0.3658761,0.001142496,0.0006386249,0.000009921629,0.0004694168],"genre_scores_gemma":[0.9835529,0.002338067,0.000960062,0.002674893,0.0007844495,0.00002176017,0.00009774296,0.0001517932,0.009418387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4016997,"threshold_uncertainty_score":0.9999352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00891188787814316,"score_gpt":0.2529240411128857,"score_spread":0.2440121532347426,"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."}}