{"id":"W4386285703","doi":"10.1148/ryai.230034","title":"The RSNA Cervical Spine Fracture CT Dataset","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre; Queen's University; University of Toronto; St. Michael's Hospital","funders":"Genentech; National Institutes of Health; National Cancer Institute; Radiological Society of North America","keywords":"Cervical spine; Medicine; Nuclear medicine; Fracture (geology); Radiology; Geology; Surgery; Geotechnical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008034414,0.001839791,0.001477171,0.004173575,0.0007094432,0.001275084,0.002621086,0.002517195,0.01759244],"category_scores_gemma":[0.006316136,0.0005567173,0.001645298,0.003418431,0.0005071691,0.0005983844,0.00130042,0.001258406,0.02644765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317278,"about_ca_system_score_gemma":0.002568861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04807647,"about_ca_topic_score_gemma":0.08439814,"domain_scores_codex":[0.9990662,0.0001138769,0.0001255215,0.0002889201,0.0002709369,0.0001346163],"domain_scores_gemma":[0.9984125,0.0002939676,0.0001197983,0.0004365538,0.0005964368,0.0001406894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006230889,0.0001711371,0.01587653,0.001253862,0.0004834846,0.0007881834,0.00005275783,0.002445449,0.001225298,0.0003725418,0.9431824,0.03352524],"study_design_scores_gemma":[0.001430759,0.0003865848,0.1624838,0.001577264,0.001008206,0.0107748,0.0005158661,0.01450566,0.004348378,0.003846438,0.7988311,0.0002911452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009523655,0.001171382,0.0006825162,0.0003418123,0.0001244489,0.0001374822,0.9849389,0.001099204,0.001980686],"genre_scores_gemma":[0.007606083,0.0003259998,0.0009168801,0.00009242334,0.00005625848,0.0001366061,0.9899677,0.00007441206,0.0008236051],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04807647,"threshold_uncertainty_score":0.09559327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03080931013488404,"score_gpt":0.3007360930376755,"score_spread":0.2699267829027915,"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."}}