{"id":"W2943514345","doi":"10.1017/s1460396919000098","title":"Evaluation of plan optimisers in prostate VMAT using the dose distribution index","year":2019,"lang":"en","type":"article","venue":"Journal of Radiotherapy in Practice","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Grand River Hospital; Princess Margaret Cancer Centre; University of Waterloo; University of Toronto; University Health Network","funders":"","keywords":"Nuclear medicine; Radiation treatment planning; Medicine; Weighting; Prostate; Medical physics; Plan (archaeology); Radiation therapy; Radiology; Cancer","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":[0.002287355,0.0006773253,0.0004933131,0.001208768,0.0002515873,0.0006347821,0.000493051,0.000379058,0.001426177],"category_scores_gemma":[0.005406418,0.0004917081,0.0006811633,0.0008225901,0.000301984,0.000530816,0.000628359,0.0004542261,0.0001958784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736342,"about_ca_system_score_gemma":0.0005262669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002271984,"about_ca_topic_score_gemma":0.001576265,"domain_scores_codex":[0.9988907,0.0003884979,0.0000862289,0.0001440835,0.0004336194,0.00005681506],"domain_scores_gemma":[0.9973265,0.001734159,0.0002889428,0.0002374296,0.0003482578,0.00006471839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001534445,0.0002541193,0.02657603,0.0003458068,0.0003249425,0.00007500189,0.0003347838,0.7547597,0.04082191,0.001765359,0.000744108,0.1724637],"study_design_scores_gemma":[0.000229185,0.001823642,0.04171996,0.00003591298,0.0001657836,0.0003774436,0.00009177611,0.876792,0.07283561,0.001591956,0.004237471,0.00009938707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7426693,0.001414128,0.2445076,0.0001269694,0.0000410299,0.0005807888,0.0006849117,0.002112691,0.007862537],"genre_scores_gemma":[0.9132191,0.0001360769,0.08486026,0.00002149288,0.000009057541,0.0001632192,0.0004874881,0.0004202117,0.0006829861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002287355,"threshold_uncertainty_score":0.0125981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216833154955598,"score_gpt":0.357506044967184,"score_spread":0.335337713417628,"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."}}