{"id":"W3134259292","doi":"10.3389/fonc.2021.730375","title":"Corrigendum: Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy","year":2021,"lang":"en","type":"erratum","venue":"Frontiers in Oncology","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Brachytherapy; Dose rate; Cervical cancer; Medical physics; Radiation oncology; Selection (genetic algorithm); Medicine; Radiation therapy; Computer science; Oncology; Radiology; Internal medicine; Artificial intelligence; 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":[0.002694067,0.00223131,0.002162212,0.002113126,0.001756302,0.002830094,0.003400668,0.005363863,0.08891597],"category_scores_gemma":[0.04362094,0.000909783,0.002228518,0.001575998,0.001507931,0.00209184,0.001676171,0.005535435,0.0370618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003244808,"about_ca_system_score_gemma":0.003879588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02278933,"about_ca_topic_score_gemma":0.03954687,"domain_scores_codex":[0.9976736,0.0005002154,0.0002609032,0.0003689076,0.001043048,0.0001533228],"domain_scores_gemma":[0.9851383,0.004271427,0.000464402,0.0007283178,0.008814213,0.0005834377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002618247,0.000005082234,0.00006551198,0.0001542532,0.00001556149,0.0001791148,0.00001548284,0.0004269409,0.00007327268,0.0009678878,0.9860079,0.01206272],"study_design_scores_gemma":[0.00006730009,0.00004903129,0.0007012209,0.0004414437,0.0001167054,0.0009750642,0.000062717,0.00574562,0.001096513,0.005777965,0.9848712,0.00009515358],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.000329257,0.003895414,0.01174059,0.05664138,0.9174168,0.00005977765,0.002418302,0.001311206,0.006187285],"genre_scores_gemma":[0.02537385,0.02146789,0.0522981,0.08946873,0.3744538,0.0005489434,0.009057372,0.007124837,0.4202066],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.08891597,"threshold_uncertainty_score":0.2974536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131803972609837,"score_gpt":0.3139798680189173,"score_spread":0.2826618282928189,"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."}}