{"id":"W2903408278","doi":"10.1148/radiol.2018180736","title":"The RSNA Pediatric Bone Age Machine Learning Challenge","year":2018,"lang":"en","type":"article","venue":"Radiology","topic":"Autopsy Techniques and Outcomes","field":"Medicine","cited_by":454,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"Leidos; National Cancer Institute; National Institute for Health and Care Research; Radiological Society of North America; Massachusetts General Hospital","keywords":"Medicine; Artificial intelligence; Machine learning; Test set; Convolutional neural network; Bone age; Radiological weapon; Artificial neural network; Set (abstract data type); Upload; Deep learning; Data set; Test (biology); Radiography; Medical physics; Radiology; Computer science; Internal medicine","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":[0.0003052684,0.00009819631,0.0002369524,0.00005377188,0.0001929179,0.000005420222,0.0000924016,0.0001179277,0.0001651278],"category_scores_gemma":[0.0001754708,0.00005807691,0.00007088982,0.00008913375,0.0001621397,0.00001434573,0.0000517862,0.0002995654,0.0001152192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002844643,"about_ca_system_score_gemma":0.00002356885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002044376,"about_ca_topic_score_gemma":0.00001979353,"domain_scores_codex":[0.9992488,0.00007269253,0.0001693384,0.0001640785,0.00007209495,0.0002729887],"domain_scores_gemma":[0.9994597,0.0001442586,0.00006461199,0.0002386243,0.00003076822,0.00006204046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008822065,0.0003026672,0.668586,0.0001961464,0.0003155282,0.002073448,0.003221902,0.00000254267,0.00335254,0.07748842,0.07005735,0.1735212],"study_design_scores_gemma":[0.000984612,0.002011295,0.1204631,0.00000766998,0.0001023765,0.0008946129,0.00004995162,0.0005107087,0.0001675056,0.001568617,0.8730659,0.0001735856],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8886961,0.02071184,0.001956044,0.01981286,0.001100779,0.000660673,0.00000344269,0.0008991904,0.06615907],"genre_scores_gemma":[0.9863398,0.003631544,0.0007665732,0.0006228581,0.001420872,0.00001244895,0.000009429363,0.00001948831,0.00717695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8030086,"threshold_uncertainty_score":0.2368308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01874844020448894,"score_gpt":0.2910887746923837,"score_spread":0.2723403344878947,"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."}}