{"id":"W4246937588","doi":"10.5489/cuaj.734","title":"HIFU: Definitely ready for prime time","year":2013,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Prime time; Prime (order theory); Computer science; Medicine; Mathematics; Telecommunications; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002231809,0.0004302613,0.0003059766,0.000480077,0.001079151,0.002153077,0.0005856705,0.002057319,0.1394402],"category_scores_gemma":[0.006080057,0.0003179896,0.0003725828,0.0002305779,0.0007432562,0.00254034,0.001708723,0.002998231,0.04211851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006150418,"about_ca_system_score_gemma":0.001816957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001178008,"about_ca_topic_score_gemma":0.002245963,"domain_scores_codex":[0.9993011,0.0001228247,0.00001586486,0.00005908506,0.0003120477,0.0001892248],"domain_scores_gemma":[0.9975037,0.0003527694,0.0001062968,0.0002292564,0.000539784,0.001268287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000372132,0.00004150673,0.0006388504,0.0001047087,0.00001156635,0.0001992285,0.00008272866,0.0001390523,0.00233763,0.004839798,0.831793,0.1594398],"study_design_scores_gemma":[0.00006545258,0.0001509933,0.0009354544,0.00006623504,0.000007383054,0.0006693656,0.00006274998,0.000179941,0.002237875,0.006482532,0.9891165,0.00002549702],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.01073678,0.02282363,0.1031613,0.3345609,0.1085767,0.0005431614,0.005514293,0.02113495,0.3929481],"genre_scores_gemma":[0.2027169,0.009922368,0.09537032,0.08620272,0.06183358,0.0005334122,0.006352955,0.01015763,0.5269101],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.1394402,"threshold_uncertainty_score":0.4664738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0145291718840977,"score_gpt":0.2525608013312605,"score_spread":0.2380316294471628,"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."}}