{"id":"W2768284241","doi":"10.1038/s41598-017-16823-2","title":"In-vivo longitudinal imaging of microvascular changes in irradiated oral mucosa of radiotherapy cancer patients using optical coherence tomography","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Laser Applications in Dentistry and Medicine","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"Ministry of Education and Science of the Russian Federation; Russian Foundation for Basic Research","keywords":"Optical coherence tomography; In vivo; Radiation therapy; Preclinical imaging; Medicine; Oral mucosa; Cancer; Tomography; Irradiation; Radiology; Pathology; Internal medicine; Biology; Physics","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":[0.0008216013,0.0001142865,0.0003790896,0.0003322031,0.0000957942,0.00003104752,0.0001503041,0.00005129182,0.0001456995],"category_scores_gemma":[0.0001275716,0.0001002378,0.00007526446,0.000445455,0.0007190077,0.0001375769,0.00004888019,0.0001108913,2.395138e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007651426,"about_ca_system_score_gemma":0.0001549865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008997917,"about_ca_topic_score_gemma":0.0003166091,"domain_scores_codex":[0.9983828,0.00002042057,0.000507543,0.0004503376,0.0004075576,0.0002313503],"domain_scores_gemma":[0.9982924,0.00001372095,0.0004498767,0.0009178559,0.0002394507,0.00008665601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003155232,0.0002753987,0.8210407,0.0001054984,0.00002650492,0.0002363685,0.0001417427,0.00001259389,0.1765743,0.000006905995,0.0002648119,0.001283558],"study_design_scores_gemma":[0.001329446,0.0000422101,0.7138643,0.0006071088,0.0000695889,0.0001077192,0.00003909653,0.0003780666,0.2823892,0.0001363038,0.000930056,0.0001069082],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970611,0.0005854674,0.00006586917,0.0001582364,0.001537015,0.0004593152,0.000005305605,0.000006691063,0.0001209368],"genre_scores_gemma":[0.9984623,0.00003541724,0.001268409,0.00002040621,0.00003913946,0.00002462261,0.00001319892,0.000009719178,0.0001268475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1071765,"threshold_uncertainty_score":0.4087577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502295382389699,"score_gpt":0.3312247921784364,"score_spread":0.3062018383545394,"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."}}