{"id":"W4410722148","doi":"10.1002/mp.17872","title":"Reference datasets for commissioning of model‐based dose calculation algorithms for electronic brachytherapy","year":2025,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Université Laval; Princess Margaret Cancer Centre; Carleton University; University Health Network; University of Toronto; McGill University","funders":"European Regional Development Fund; Generalitat Valenciana; Agencia Estatal de Investigación; Junta de Castilla y León; Carl Zeiss Meditec AG; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Imaging phantom; Brachytherapy; Computer science; Algorithm; Radiation treatment planning; Radiance; Dosimetry; Calibration; Monte Carlo method; Medical physics; Software; Nuclear medicine; Mathematics; Physics; Radiation therapy; Radiology; Medicine; Optics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004481127,0.001497157,0.0008292651,0.003288247,0.0009552126,0.001807416,0.003829592,0.002279054,0.02786018],"category_scores_gemma":[0.01451797,0.0007301797,0.001274206,0.004672761,0.0005286041,0.001546651,0.001870469,0.001823029,0.02041635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002518562,"about_ca_system_score_gemma":0.002257333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008796982,"about_ca_topic_score_gemma":0.01529236,"domain_scores_codex":[0.9965228,0.0006293863,0.0004774203,0.0004958899,0.00171552,0.0001590159],"domain_scores_gemma":[0.9871823,0.00349679,0.0007392045,0.003254015,0.005110809,0.0002169731],"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":[0.0007219447,0.0005671767,0.008364079,0.00332856,0.0002415909,0.0002713139,0.0002361681,0.06038173,0.006194104,0.007742246,0.7726647,0.1392865],"study_design_scores_gemma":[0.0004952073,0.0002199766,0.01314103,0.001091361,0.0001234441,0.000586672,0.0002277869,0.0502349,0.02743324,0.007105142,0.8991341,0.0002072127],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01454074,0.001126681,0.09045907,0.0008677869,0.0003851611,0.0009182708,0.8357098,0.02324813,0.03274441],"genre_scores_gemma":[0.02485947,0.0005113489,0.05445449,0.0002909643,0.00004093505,0.001768335,0.910496,0.004380515,0.003197928],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02786018,"threshold_uncertainty_score":0.09320152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278216343224658,"score_gpt":0.3710529226781748,"score_spread":0.343231288355709,"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."}}