{"id":"W2770940010","doi":"10.1017/s1460396917000619","title":"Dosimetric variations in calculation grid size in prostate VMAT: a dose-volume histogram analysis using the Gaussian error function","year":2017,"lang":"en","type":"article","venue":"Journal of Radiotherapy in Practice","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":2,"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; Dose-volume histogram; Volume (thermodynamics); Prostate; Radiation treatment planning; Medicine; Histogram; Dosimetry; Coefficient of variation; Radiation therapy; Mathematics; Statistics; Radiology; Computer science; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001727649,0.0001801376,0.0004235481,0.0008743825,0.0002234969,0.0002114573,0.000329923,0.00006515418,0.0001040005],"category_scores_gemma":[0.0002629622,0.0001428852,0.000186239,0.001412709,0.00006535023,0.002030928,0.0000151544,0.0006632963,3.346382e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006202378,"about_ca_system_score_gemma":0.0001492923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856628,"about_ca_topic_score_gemma":0.0001450545,"domain_scores_codex":[0.9980329,0.0004306492,0.0007736011,0.0001983914,0.0003142742,0.0002502063],"domain_scores_gemma":[0.9969048,0.0005101632,0.001958654,0.0004385001,0.0001374546,0.00005045399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001054423,0.0008708742,0.8900319,0.000008899441,0.001092026,0.00004819877,0.003331795,0.06333151,0.004452554,0.00101434,0.0001044912,0.03465904],"study_design_scores_gemma":[0.004642516,0.0004000883,0.8203784,0.0001157955,0.0006972846,0.00006121772,0.000681486,0.1476565,0.0001284958,0.00126461,0.02350551,0.0004681038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3145933,0.001511155,0.6772398,0.004444377,0.0006474158,0.0008946268,0.00001278026,0.00002302352,0.0006335084],"genre_scores_gemma":[0.9352455,0.0002182067,0.06393263,0.0001398546,0.0003385482,0.00001911786,0.000002261451,0.00002873252,0.00007511576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6206523,"threshold_uncertainty_score":0.5826691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164202974792266,"score_gpt":0.3473278096932857,"score_spread":0.3309075122140591,"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."}}