{"id":"W4294933008","doi":"10.1177/15330338221124695","title":"Comparison of Prospectively Generated Glioma Treatment Plans Clinically Delivered on Magnetic Resonance Imaging (MRI)-Linear Accelerator (MR-Linac) Versus Conventional Linac: Predicted and Measured Skin Dose","year":2022,"lang":"en","type":"article","venue":"Technology in Cancer Research & Treatment","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Linear particle accelerator; Magnetic resonance imaging; Nuclear medicine; Radiation treatment planning; Monte Carlo method; Radiation therapy; Dosimeter; Dosimetry; Imaging phantom; Medicine; Medical physics; Physics; Nuclear magnetic resonance; Radiology; Optics; Beam (structure); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003362009,0.0003379409,0.0006126342,0.0006343843,0.0003768368,0.00001976716,0.0003474469,0.000105925,0.0003681986],"category_scores_gemma":[0.00001901194,0.000310097,0.00009702994,0.00102461,0.0005534516,0.00006943963,0.0001662459,0.0005807558,0.000001670863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002321696,"about_ca_system_score_gemma":0.0004940823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001341991,"about_ca_topic_score_gemma":0.0001802113,"domain_scores_codex":[0.9971052,0.0003135872,0.0006246968,0.000800339,0.0005520981,0.0006040861],"domain_scores_gemma":[0.9986881,0.0002388589,0.0002184655,0.0005008279,0.0002597033,0.00009406386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01139071,0.007642871,0.6985857,0.00001873418,0.0005185041,0.0001440854,0.001008743,0.001268393,0.02890455,0.004094028,0.0003421531,0.2460815],"study_design_scores_gemma":[0.07848163,0.0740566,0.09792641,0.0004081982,0.0002803623,0.00002582846,0.004629436,0.03668819,0.6684897,0.007312091,0.02998939,0.001712156],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893753,0.00614427,0.0001922061,0.0009123769,0.0001101759,0.002029836,0.0009480349,0.0001776129,0.000110216],"genre_scores_gemma":[0.9909483,0.0006503128,0.003502959,0.000007318125,0.00007779343,0.004536782,0.00009246505,0.00004917165,0.0001349599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6395851,"threshold_uncertainty_score":0.9999351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08036365742159217,"score_gpt":0.435013220937771,"score_spread":0.3546495635161788,"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."}}