{"id":"W4220774884","doi":"10.1016/j.brachy.2022.01.009","title":"Targeting prostate lesions on multiparametric MRI with HDR brachytherapy: Optimal planning margins determined using whole-mount digital histology","year":2022,"lang":"en","type":"article","venue":"Brachytherapy","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"","keywords":"Medicine; Brachytherapy; Histology; Prostate cancer; Margin (machine learning); Radiation treatment planning; Prostatectomy; Nuclear medicine; Multiparametric MRI; Radiology; Target lesion; Magnetic resonance imaging; Lesion; Prostate; Cancer; Surgical margin; Catheter; Radiation therapy; Surgery; Pathology; 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":[0.0006594358,0.0004254885,0.0002603239,0.0006051559,0.0002560237,0.0009065031,0.0004468715,0.0005802789,0.001253778],"category_scores_gemma":[0.00133416,0.0004959889,0.0002644059,0.0002061996,0.000348426,0.0005404526,0.0004740531,0.0003682295,0.0004433784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003357967,"about_ca_system_score_gemma":0.0004873126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00157999,"about_ca_topic_score_gemma":0.003715245,"domain_scores_codex":[0.9998164,0.00004734048,0.00001249738,0.00003311669,0.00007246828,0.00001827294],"domain_scores_gemma":[0.9997404,0.0000899976,0.00005742142,0.00004712406,0.00004539351,0.00001955444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001014458,0.0001109508,0.02742256,0.0007313326,0.0001281599,0.001173134,0.000385608,0.02858641,0.7478263,0.001899882,0.001178221,0.1895429],"study_design_scores_gemma":[0.0001200671,0.001189256,0.1301762,0.0002489609,0.0004990858,0.0134846,0.0003549939,0.178877,0.6577967,0.002541417,0.01451905,0.0001926404],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5497026,0.00671916,0.4284396,0.0006422838,0.0001064712,0.0002326653,0.0003934277,0.002000399,0.01176335],"genre_scores_gemma":[0.8764387,0.001290101,0.119523,0.0000949765,0.00002063,0.00003845677,0.00008563424,0.000235271,0.002273306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00157999,"threshold_uncertainty_score":0.004194319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02736565257576511,"score_gpt":0.2956616290703009,"score_spread":0.2682959764945358,"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."}}