{"id":"W4408021197","doi":"10.1016/j.urolonc.2024.12.075","title":"STIMULATED RAMAN HISTOLOGY AND ARTIFICIAL INTELLIGENCE PROVIDE NEAR REAL-TIME PROSTATE CANCER DIAGNOSIS FROM MRI TARGETED PROSTATE BIOPSIES","year":2025,"lang":"en","type":"article","venue":"Urologic Oncology Seminars and Original Investigations","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Prostate cancer; Prostate; Histology; Multiparametric MRI; Radiology; Cancer; Pathology; Oncology; Internal medicine","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":[0.0005672011,0.0004940418,0.0002920874,0.0006919433,0.0001946631,0.001115373,0.0005523678,0.001094194,0.003185612],"category_scores_gemma":[0.001365239,0.0005416656,0.0003641085,0.0002987552,0.0004270033,0.0007777484,0.0007282603,0.0007161153,0.00161598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002337713,"about_ca_system_score_gemma":0.0002847033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005888302,"about_ca_topic_score_gemma":0.001983342,"domain_scores_codex":[0.9997203,0.0000715118,0.0000113715,0.00005000515,0.0001245732,0.00002227773],"domain_scores_gemma":[0.9995009,0.0002594471,0.00004862368,0.0000622755,0.00009638081,0.00003244885],"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":[0.0009401422,0.00008100395,0.002718701,0.0002437022,0.00008259711,0.0003671179,0.0001728818,0.01719322,0.7404863,0.001823657,0.001349633,0.234541],"study_design_scores_gemma":[0.00007138035,0.0004602711,0.01558346,0.0000535878,0.0001511184,0.002498636,0.0003031376,0.5357698,0.4322295,0.005689666,0.007074299,0.0001151647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3594935,0.00341243,0.620444,0.001209413,0.0003469854,0.0001130312,0.000503831,0.005459013,0.009017811],"genre_scores_gemma":[0.740779,0.001275506,0.2478796,0.0004501069,0.0001348975,0.00003946587,0.0003371341,0.0004588938,0.008645386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003185612,"threshold_uncertainty_score":0.01065689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152393313188793,"score_gpt":0.3385532055597908,"score_spread":0.3233138742409115,"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."}}