{"id":"W2797311821","doi":"10.1088/1361-6560/aabd14","title":"Inverse optimization of objective function weights for treatment planning using clinical dose-volume histograms","year":2018,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health; Princess Margaret Cancer Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse; Radiation treatment planning; Mathematics; Histogram; Statistic; Volume (thermodynamics); Statistics; Nuclear medicine; Mathematical optimization; Computer science; Algorithm; Medical physics; Medicine; Artificial intelligence; Radiology; Radiation therapy; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001591115,0.0006756358,0.000646206,0.0006422976,0.0001881469,0.0006705876,0.000903229,0.0006592319,0.001179491],"category_scores_gemma":[0.00571839,0.0006350242,0.0007755699,0.0004283275,0.0005196978,0.0006897213,0.0005333367,0.001089487,0.0001961875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001316583,"about_ca_system_score_gemma":0.00142212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01030218,"about_ca_topic_score_gemma":0.006433404,"domain_scores_codex":[0.9995789,0.0001447035,0.00002142918,0.00005631748,0.000163215,0.00003544847],"domain_scores_gemma":[0.998541,0.001062249,0.0001387093,0.00005941874,0.0001696894,0.00002893641],"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":[0.000009829073,0.000005303732,0.0001482016,0.000009562016,0.000005712197,0.000006189335,0.000008231087,0.9932297,0.0004051294,0.0009142663,0.00006920238,0.005188573],"study_design_scores_gemma":[0.000002825675,0.000004703212,0.00004569158,0.000001429867,0.000001507736,0.000002699323,0.000001125332,0.9989597,0.0002315798,0.0006609798,0.00008563684,0.000002138238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01014903,0.0000557264,0.9887313,0.00005296977,0.000008085901,0.00004506793,0.00003551274,0.0002545907,0.0006676791],"genre_scores_gemma":[0.5259424,0.0001474073,0.4707246,0.0001379584,0.00002787324,0.0003886929,0.0002268426,0.000420978,0.001983318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01030218,"threshold_uncertainty_score":0.02048445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2182132931928489,"score_gpt":0.4539532532084035,"score_spread":0.2357399600155545,"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."}}