{"id":"W6943779975","doi":"10.1594/essr2018/p-0057","title":"Multi-energy CT (MECT) in the acute MSK setting","year":2018,"lang":"en","type":"article","venue":"European Society of Radiology","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital","funders":"","keywords":"Computed tomography; MEDLINE; Disease; Medical imaging","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":[0.0004774363,0.000836419,0.0008222034,0.002302674,0.00127457,0.001759889,0.0006377018,0.001965307,0.005727421],"category_scores_gemma":[0.003155288,0.0004432579,0.0005883704,0.00233384,0.001172079,0.002446972,0.001291419,0.002291962,0.00128769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041327,"about_ca_system_score_gemma":0.001320121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00259165,"about_ca_topic_score_gemma":0.00312756,"domain_scores_codex":[0.9994341,0.00009728469,0.0001040874,0.00007143353,0.00006666585,0.0002263635],"domain_scores_gemma":[0.999119,0.0001912352,0.0001732699,0.0000622548,0.000114366,0.0003398872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"observational","study_design_scores_codex":[0.003162132,0.0005969331,0.2761591,0.0006844394,0.000151167,0.6458627,0.0004405534,0.00185358,0.008819795,0.001697209,0.006188885,0.05438348],"study_design_scores_gemma":[0.0001365723,0.00079403,0.1236555,0.0008429073,0.0001703143,0.8524734,0.001710764,0.003909622,0.004500175,0.004190512,0.007521354,0.00009502157],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145976,0.02787736,0.005989063,0.009241764,0.0005060745,0.0002108019,0.0006609753,0.0001147057,0.04080153],"genre_scores_gemma":[0.9868839,0.007435982,0.001899581,0.001273468,0.0008192051,0.00002624518,0.0002049789,0.00002889319,0.001427804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005727421,"threshold_uncertainty_score":0.01916009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00893158874661695,"score_gpt":0.2274043127681779,"score_spread":0.218472724021561,"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."}}