{"id":"W4200432248","doi":"10.2196/34411","title":"Correction: Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study","year":2021,"lang":"en","type":"erratum","venue":"JMIR Medical Informatics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Acute kidney injury; Contrast (vision); Intravenous contrast; Medicine; Administration (probate law); Computer science; Intravenous fluid; Anesthesia; Artificial intelligence; Intensive care medicine; Data science; Radiology; Internal medicine; Computed tomography","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":[0.005364863,0.002473613,0.001931578,0.004765709,0.002616658,0.003867957,0.003426626,0.005845894,0.1350743],"category_scores_gemma":[0.1508666,0.001300974,0.002222865,0.003420179,0.00215428,0.00254971,0.002148985,0.008520426,0.06569289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00316184,"about_ca_system_score_gemma":0.007744367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02358861,"about_ca_topic_score_gemma":0.02245847,"domain_scores_codex":[0.9932574,0.001134717,0.001766224,0.0007122403,0.002609969,0.0005194141],"domain_scores_gemma":[0.9227692,0.0216289,0.003398847,0.004903151,0.04541102,0.001888936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005036963,0.000006301565,0.0001008293,0.0001848926,0.00001439266,0.0001373603,0.00002775877,0.00004033265,0.00002880167,0.0003805938,0.9941255,0.004902788],"study_design_scores_gemma":[0.0002162337,0.000057267,0.001917214,0.001273361,0.00009263485,0.00109428,0.0001902761,0.0007136553,0.0007016571,0.00309656,0.9905702,0.00007670432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0002958487,0.0006437885,0.001285563,0.05239334,0.9325645,0.00009463462,0.00793945,0.0009560721,0.003826742],"genre_scores_gemma":[0.03443193,0.007782797,0.01522975,0.1337199,0.4154735,0.001334874,0.01597443,0.005972057,0.3700807],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1350743,"threshold_uncertainty_score":0.4518685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008610610897383408,"score_gpt":0.2479179123815729,"score_spread":0.2393073014841895,"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."}}