{"id":"W2015967407","doi":"10.1148/radiol.2541090314","title":"Synthetic–Echo Time Postprocessing Technique for Generating Images with Variable T2-weighted Contrast: Diagnosis of Meniscal and Cartilage Abnormalities of the Knee","year":2009,"lang":"en","type":"article","venue":"Radiology","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto; University Health Network; Mount Sinai Hospital","funders":"","keywords":"Medicine; Magnetic resonance imaging; Articular cartilage; Sagittal plane; Cartilage; Nuclear medicine; T2 weighted; Diagnostic accuracy; Echo time; Contrast (vision); Radiology; Osteoarthritis; Anatomy; Pathology; Artificial intelligence","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":[0.0002492937,0.0001108711,0.0004434382,0.00007098199,0.00008632102,0.000006152208,0.00005300652,0.0001264028,0.00002657623],"category_scores_gemma":[0.00006997109,0.00006546867,0.00005573872,0.00008529142,0.0003826989,0.00004687961,0.0000133167,0.00009523013,6.057324e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001553844,"about_ca_system_score_gemma":0.00006776844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001331002,"about_ca_topic_score_gemma":6.894114e-7,"domain_scores_codex":[0.9992496,0.00005235307,0.0003246135,0.0001501376,0.0000621345,0.0001611085],"domain_scores_gemma":[0.9993214,0.0001649808,0.0001987906,0.0001535303,0.0001280825,0.00003318856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003008893,0.00005380366,0.007811521,0.0002140872,0.00005286684,0.000002248099,0.0002296613,0.000003713911,0.9843598,0.001943477,0.0004270022,0.004600921],"study_design_scores_gemma":[0.0004954159,0.00120223,0.0008961495,0.0002599695,0.0001242467,0.0007789878,0.0001136354,0.0001472682,0.9945431,0.001130101,0.0002251604,0.00008368943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776994,0.001129559,0.01758893,0.000750208,0.00005160521,0.001020862,0.00006736853,0.00007350525,0.001618552],"genre_scores_gemma":[0.9457501,0.0000474495,0.05375551,0.0001281503,0.00005901447,0.00009078437,0.000004720785,0.0000109218,0.0001533614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03616658,"threshold_uncertainty_score":0.2669735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004358015134248482,"score_gpt":0.2278281849159431,"score_spread":0.2234701697816946,"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."}}