{"id":"W2050861119","doi":"10.1016/j.ijrobp.2014.05.725","title":"Inherent Margin due to Tumor Shrinkage Can Decrease Target Delineation Margin Protocols in IMRT Treatment for Head and Neck Carcinomas","year":2014,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Nuclear medicine; Radiation therapy; Head and neck cancer; Margin (machine learning); Radiation treatment planning; Head and neck; Radiation oncologist; Radiology; Surgery","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":[0.002046636,0.0002353134,0.0001751163,0.0003263474,0.0002777266,0.0003867018,0.0003826755,0.0002502891,0.0007464151],"category_scores_gemma":[0.007030346,0.0002127652,0.0003142668,0.0002569249,0.0002289946,0.0005365474,0.0005642502,0.0004339193,0.00009289614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004788093,"about_ca_system_score_gemma":0.0003555654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007305549,"about_ca_topic_score_gemma":0.001578636,"domain_scores_codex":[0.9992536,0.0002392724,0.00008916238,0.00008820331,0.0002965115,0.0000332624],"domain_scores_gemma":[0.9979306,0.001196395,0.0004553801,0.000164619,0.0002084272,0.00004449769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003553261,0.0006001446,0.06856739,0.0008655451,0.0004259824,0.0001869646,0.001475307,0.1090475,0.4448179,0.002569314,0.001301218,0.3665895],"study_design_scores_gemma":[0.0002023128,0.00900218,0.2715803,0.0001807103,0.001103319,0.001528464,0.0004432503,0.2503778,0.4506595,0.003221372,0.01157126,0.0001294905],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9511619,0.00182164,0.04463553,0.0001668841,0.00008229091,0.00005704389,0.00005965937,0.0002526268,0.001762359],"genre_scores_gemma":[0.9913475,0.0001469663,0.007820313,0.00004301158,0.00001633625,0.0000320681,0.00003342476,0.0001260889,0.0004343145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002046636,"threshold_uncertainty_score":0.01082373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03637908282759152,"score_gpt":0.3685865396395243,"score_spread":0.3322074568119328,"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."}}