{"id":"W2943291236","doi":"10.1007/s00247-019-04408-2","title":"Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning","year":2019,"lang":"en","type":"article","venue":"Pediatric Radiology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Neuroradiology; Radiography; Medical physics; Radiology; Deep learning; Artificial intelligence; Neurology; Computer 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":[],"consensus_categories":[],"category_scores_codex":[0.0005335912,0.0001808834,0.0005196563,0.0007207207,0.00009923714,0.000006769981,0.0001123876,0.0002678549,0.0002158921],"category_scores_gemma":[0.0004255812,0.0001720064,0.0001882233,0.00115894,0.00005464511,0.00008417948,0.0000285919,0.0004029683,0.0001440293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007753013,"about_ca_system_score_gemma":0.0002437639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003549486,"about_ca_topic_score_gemma":0.000004454146,"domain_scores_codex":[0.9980568,0.0001852604,0.0007205904,0.0003641151,0.0002015658,0.000471696],"domain_scores_gemma":[0.9986048,0.0004216411,0.0003275117,0.0002676026,0.0002260739,0.0001523376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004922994,0.0000773622,0.9921035,0.0005273343,0.00003576603,0.000009091529,0.0005722885,0.002415644,0.001703895,0.00003709884,0.0001254608,0.002343396],"study_design_scores_gemma":[0.001386259,0.004286234,0.5432045,0.00008713165,0.00355185,0.00124493,0.003836164,0.4380904,0.001688017,0.0007194806,0.0004720561,0.001432928],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929292,0.004386341,0.0004551665,0.000119777,0.0012791,0.0004327861,0.000001055426,0.00022996,0.0001666757],"genre_scores_gemma":[0.9936428,0.002322826,0.002111441,0.00005629378,0.001741292,0.000008285432,0.00002978595,0.00003278192,0.00005447559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4488989,"threshold_uncertainty_score":0.7014217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04811493225171452,"score_gpt":0.3742277724411051,"score_spread":0.3261128401893905,"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."}}