{"id":"W4366280062","doi":"10.1038/s41597-023-02136-9","title":"Author Correction: MIMIC-IV, a freely accessible electronic health record dataset","year":2023,"lang":"en","type":"erratum","venue":"Scientific Data","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Electronic health record; Computer science; Information retrieval; Health records; World Wide Web; Data science; Health care; Political science","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":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.008393578,0.0006815785,0.0008828353,0.001169772,0.00196795,0.004105445,0.02308062,0.0005194693,0.0003604417],"category_scores_gemma":[0.001627732,0.0006899359,0.0001182696,0.004922405,0.00027874,0.002091074,0.01192761,0.004208623,0.003690867],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006983611,"about_ca_system_score_gemma":0.01001349,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007049417,"about_ca_topic_score_gemma":0.02300481,"domain_scores_codex":[0.9884793,0.0009141797,0.00121419,0.005179976,0.001972671,0.002239664],"domain_scores_gemma":[0.9806623,0.0003029919,0.001210253,0.01692548,0.0002702893,0.0006287275],"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":[0.000003683687,0.00004420385,0.00005363272,0.0003555808,0.0000307128,0.00002459141,0.0001349244,0.00001186949,6.380936e-7,0.0003374119,0.9290498,0.06995292],"study_design_scores_gemma":[0.0001170307,0.0001203689,0.0002195949,0.0004011539,0.00001504568,0.00003141311,0.00002488118,0.1603359,7.383119e-7,0.0006766887,0.8375406,0.0005165652],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.000005374162,0.004532326,0.04484897,0.04453927,0.7945916,0.001771039,0.1033055,0.002969892,0.003435991],"genre_scores_gemma":[0.00001727655,0.000230634,0.00602559,0.0009453981,0.00180988,0.0000638714,0.3704989,0.000112218,0.6202963],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.7927818,"threshold_uncertainty_score":0.9995627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1006169606146817,"score_gpt":0.3922536860077259,"score_spread":0.2916367253930442,"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."}}