{"id":"W4205996145","doi":"10.1148/ryai.210099","title":"Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning","year":2022,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Convolutional neural network; Artificial intelligence; Radiography; Pipeline (software); Computer science; Machine learning; Radiology","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":[0.0003347871,0.0001250674,0.0002670443,0.0002653419,0.0002365571,0.00001221375,0.00009922986,0.00005989049,0.000423778],"category_scores_gemma":[0.0001182011,0.0001273551,0.00006884136,0.0005171855,0.0001319122,0.00004374159,0.00003115169,0.0003404845,0.000004242499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004398292,"about_ca_system_score_gemma":0.000009306455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006636648,"about_ca_topic_score_gemma":0.00001105288,"domain_scores_codex":[0.9989006,0.0002007745,0.0003613674,0.0001983868,0.0001536348,0.0001852463],"domain_scores_gemma":[0.9996382,0.00009865269,0.00005294934,0.0001164603,0.00002524732,0.00006851674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002308246,0.00004639041,0.002705529,0.000103151,0.00009055327,0.00001191023,0.0005397135,0.777643,0.06189849,0.0003323681,0.000009851509,0.156596],"study_design_scores_gemma":[0.00003762689,0.000122086,0.0002337233,0.00001961189,0.00004923044,0.00003956243,0.0001938093,0.9789029,0.01971367,0.0004324518,0.000139694,0.0001156176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6179687,0.0006726756,0.3808752,0.00004784248,0.0001997064,0.00007078202,0.000004522754,0.0001254091,0.0000351698],"genre_scores_gemma":[0.9993139,0.0001034635,0.0004330289,0.00004687084,0.00005139241,0.000009004304,0.00001629909,0.00001789169,0.000008151243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3813452,"threshold_uncertainty_score":0.519339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155225206151181,"score_gpt":0.2490837630609419,"score_spread":0.2275315109994301,"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."}}