{"id":"W4229082130","doi":"10.1212/wnl.0000000000200293","title":"Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia","year":2022,"lang":"en","type":"erratum","venue":"Neurology","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Children's Hospital; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Cortical dysplasia; Multicenter study; Deep learning; Artificial intelligence; Computer science; Algorithm; Dysplasia; Medicine; Machine learning; Pathology; Radiology; Magnetic resonance imaging","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":[],"consensus_categories":[],"category_scores_codex":[0.0001967189,0.0001601432,0.000500383,0.0002400088,0.0001022772,0.00000744231,0.00007098042,0.0002712909,0.0002589549],"category_scores_gemma":[0.0004242391,0.000156087,0.0002559146,0.0001403924,0.00007665644,0.00002019336,0.00004923287,0.001521271,0.000007313753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002819879,"about_ca_system_score_gemma":0.00003972587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004707663,"about_ca_topic_score_gemma":0.000007848864,"domain_scores_codex":[0.9985906,0.000239871,0.0003418927,0.0003698915,0.0002294154,0.0002282959],"domain_scores_gemma":[0.9991992,0.0001996002,0.0002342751,0.0001710457,0.0001332221,0.00006262134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006200107,0.001171249,0.009272291,0.001485607,0.001663037,0.0006478356,0.0006240646,0.0007057471,0.017593,0.00001838549,0.05894444,0.9016742],"study_design_scores_gemma":[0.001820434,0.006368485,0.004019447,0.00002759244,0.001960178,0.0003382461,0.00003414032,0.6362799,0.001266466,0.00002114323,0.347665,0.0001988592],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1897574,0.001947909,0.7193654,0.007922607,0.06440679,0.003370433,0.0001160586,0.0006795205,0.01243388],"genre_scores_gemma":[0.9282393,0.000498182,0.00549461,0.002821871,0.005121226,0.0004841922,0.003866032,0.0003230237,0.05315154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9014754,"threshold_uncertainty_score":0.6609251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108347523431087,"score_gpt":0.2803368156201931,"score_spread":0.2695020632770844,"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."}}