{"id":"W4410156427","doi":"10.1007/s00330-025-11644-8","title":"Radiological evaluation and clinical implications of deep learning- and MRI-based synthetic CT for the assessment of cervical spine injuries","year":2025,"lang":"en","type":"article","venue":"European Radiology","topic":"Spinal Fractures and Fixation Techniques","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Neuroradiology; Radiological weapon; Interventional radiology; Cervical spine; Radiology; Magnetic resonance imaging; Medical physics; Neurology; Surgery","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":[0.001572502,0.00007219524,0.0002803893,0.00006181378,0.00006325367,0.000003682819,0.00006342474,0.00004262508,0.0000275447],"category_scores_gemma":[0.001055309,0.0000443728,0.00006821981,0.00006893094,0.0004126467,0.000009005503,0.00003506872,0.000162828,1.713894e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001395072,"about_ca_system_score_gemma":0.00004538993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002021294,"about_ca_topic_score_gemma":0.000001026349,"domain_scores_codex":[0.9988304,0.0004669001,0.0003855496,0.0001817182,0.00005665713,0.00007879922],"domain_scores_gemma":[0.9987071,0.0007979742,0.0001605635,0.0001615197,0.0001424139,0.00003049892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000377152,0.0002421842,0.7193027,0.0001941574,0.0002508771,0.000001952941,0.00004860318,0.00009737259,0.003993023,0.03970918,0.0006746309,0.2351081],"study_design_scores_gemma":[0.0008188013,0.001415415,0.9803212,0.00002862376,0.0002530179,0.00002096942,0.00002693841,0.01047409,0.0001910747,0.0007971792,0.005614666,0.00003799343],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6802378,0.002966815,0.2948045,0.0169067,0.0001169951,0.001561658,0.000009750711,0.00006278736,0.00333297],"genre_scores_gemma":[0.9925618,0.0004725679,0.006398229,0.0004373378,0.00004731815,0.00002798769,0.0000226824,0.000006546932,0.00002550035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.312324,"threshold_uncertainty_score":0.180947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03307797113178784,"score_gpt":0.4233541574962759,"score_spread":0.3902761863644881,"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."}}