{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01455018,0.001354029,0.001469649,0.006680625,0.00329884,0.005419677,0.002944934,0.002675139,0.1477972],"category_scores_gemma":[0.2231016,0.0009783217,0.001372083,0.008713432,0.002020705,0.00266577,0.005056413,0.00502034,0.07938503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00351738,"about_ca_system_score_gemma":0.01180955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02091207,"about_ca_topic_score_gemma":0.02797905,"domain_scores_codex":[0.9830346,0.003654749,0.004126071,0.002014678,0.006552644,0.0006172835],"domain_scores_gemma":[0.8618186,0.03985976,0.007087456,0.01953425,0.06931688,0.002383071],"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":[0.0000298753,0.00000315151,0.0002130904,0.0001484503,0.00001449995,0.00009809568,0.00004843079,0.00004760157,0.00003413402,0.0009638288,0.9934237,0.004975156],"study_design_scores_gemma":[0.00006624313,0.00001034291,0.001075847,0.0005945654,0.00005014634,0.0003567891,0.0001521636,0.0002466284,0.0003565661,0.002752822,0.9942917,0.00004622015],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.001876028,0.001684488,0.01039275,0.1016115,0.6732666,0.0002879503,0.1866278,0.004966923,0.01928585],"genre_scores_gemma":[0.05687463,0.006733106,0.04885465,0.08307681,0.07551968,0.002112972,0.3316283,0.02113256,0.3740674],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1477972,"threshold_uncertainty_score":0.4944308,"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."}}