{"id":"W2885980814","doi":"10.1002/mrm.27372","title":"Evaluating the accuracy of multicomponent <scp>T</scp><sub>2</sub> parameters for luminal water imaging of the prostate with acceleration using inner‐volume 3<scp>D</scp><scp>GRASE</scp>","year":2018,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Imaging phantom; Prostate; Nuclear magnetic resonance; Relaxation (psychology); Chemistry; Magnetic resonance imaging; Spin echo; Volume (thermodynamics); Monte Carlo method; Prostate cancer; Nuclear medicine; Accuracy and precision; Analytical Chemistry (journal); Biomedical engineering; Physics; Mathematics; Chromatography; Medicine; Statistics; Radiology; Cancer; Thermodynamics","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":[0.002471451,0.0005892891,0.0003020033,0.0004739078,0.0002088656,0.0006028487,0.0004224833,0.0009990389,0.000488237],"category_scores_gemma":[0.01245498,0.000390161,0.000315698,0.0001907348,0.0003662163,0.0005629293,0.0003575723,0.0003406389,0.0002345581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002354171,"about_ca_system_score_gemma":0.0003551868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001729182,"about_ca_topic_score_gemma":0.002010867,"domain_scores_codex":[0.9994667,0.000287622,0.00002757252,0.00008634266,0.0001107716,0.00002097203],"domain_scores_gemma":[0.996743,0.002121452,0.0004224085,0.0003693836,0.000271356,0.00007235874],"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":[0.003836404,0.0003356407,0.0707486,0.000563438,0.0004402171,0.0003347274,0.0008398574,0.1891386,0.495593,0.001032013,0.0005562265,0.2365813],"study_design_scores_gemma":[0.00007906501,0.001169238,0.04932183,0.00004468046,0.0002229826,0.0009719186,0.0001338832,0.750577,0.1954413,0.0009086507,0.0009960142,0.0001334299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8589767,0.0008419467,0.1385635,0.0001722572,0.00002074916,0.00003990427,0.00007116643,0.0007963908,0.0005172812],"genre_scores_gemma":[0.9317693,0.0001806187,0.06754526,0.00003114574,0.000009151376,0.00001510363,0.00007951107,0.0001201147,0.0002498499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002471451,"threshold_uncertainty_score":0.0130704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04500213840423904,"score_gpt":0.3230823433581367,"score_spread":0.2780802049538977,"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."}}