{"id":"W4414751436","doi":"10.1016/j.brachy.2025.08.005","title":"Evaluation of a commercial deep-learning-based contouring software for CT-based gynecological brachytherapy","year":2025,"lang":"en","type":"article","venue":"Brachytherapy","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; London Health Sciences Foundation; Ontario Institute for Cancer Research","keywords":"Contouring; Workflow; Brachytherapy; Software; Software tool","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001135102,0.0002291491,0.0006138912,0.0002359417,0.0001222746,0.00001758264,0.0001122697,0.0001210604,0.001252266],"category_scores_gemma":[0.0009627021,0.0001861557,0.0003082374,0.0006002777,0.00007193225,0.00004565486,0.00001000438,0.00019513,0.000007035315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003082538,"about_ca_system_score_gemma":0.0005251534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005310064,"about_ca_topic_score_gemma":0.00007985863,"domain_scores_codex":[0.9979625,0.000292249,0.0004373343,0.0003685531,0.0006369377,0.0003024662],"domain_scores_gemma":[0.9975308,0.001105288,0.0001956086,0.000274048,0.0007943504,0.00009996018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005903683,0.001341693,0.1462835,0.0002222868,0.0004706948,0.000007402532,0.00004868424,0.002540358,0.001137868,0.0001499637,0.0001527113,0.8417411],"study_design_scores_gemma":[0.1611762,0.01072157,0.7002255,0.0006033955,0.001851674,0.000006846982,0.0001013669,0.05037907,0.03811799,0.003443659,0.03268507,0.0006876067],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709332,0.003719905,0.01926411,0.001578601,0.0004235222,0.002982958,0.00002267114,0.000165799,0.0009092416],"genre_scores_gemma":[0.9926892,0.0000398793,0.003231609,0.002928416,0.0001397079,0.000618689,0.0001275942,0.00002925779,0.0001956599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8410535,"threshold_uncertainty_score":0.9996607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04685632884114223,"score_gpt":0.3626082405439882,"score_spread":0.3157519117028459,"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."}}