{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00183319,0.0003583595,0.0001210657,0.0007529663,0.0001057116,0.0002688022,0.0002812234,0.0002348954,0.0007842474],"category_scores_gemma":[0.006321819,0.0001483488,0.0001390236,0.0001946529,0.0002070941,0.0002397241,0.0001977572,0.0001332413,0.000345117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007611982,"about_ca_system_score_gemma":0.000333057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001579766,"about_ca_topic_score_gemma":0.0002843005,"domain_scores_codex":[0.9995939,0.0001670663,0.00005429786,0.00004931496,0.0001199317,0.00001554945],"domain_scores_gemma":[0.9982274,0.0007830141,0.0002702294,0.0002274264,0.0004202393,0.00007178293],"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":[0.002913438,0.0004256572,0.1363592,0.0004421367,0.0001437142,0.001448711,0.0003198815,0.003916332,0.497972,0.0004461619,0.001050603,0.3545622],"study_design_scores_gemma":[0.0003722739,0.005102362,0.330673,0.00009704837,0.0003361977,0.0528491,0.0002549431,0.1345478,0.4653427,0.0007272189,0.009549269,0.0001479898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8228155,0.0006156719,0.1751039,0.00008076967,0.00003234444,0.0001403846,0.0001047037,0.0004097631,0.0006969578],"genre_scores_gemma":[0.7816674,0.0002674301,0.2172566,0.0000586589,0.00003792219,0.0001174942,0.0001905877,0.00004593011,0.000358001],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00183319,"threshold_uncertainty_score":0.009694934,"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."}}