{"id":"W2782516324","doi":"10.1038/s41598-017-18635-w","title":"Preclinical longitudinal imaging of tumor microvascular radiobiological response with functional optical coherence tomography","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Vale (Canada); Princess Margaret Cancer Centre; University of Toronto; University Health Network; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Medicine; Optical coherence tomography; In vivo; Biomarker; Radiation therapy; Preclinical imaging; Functional imaging; Pathology; Nuclear medicine; Radiology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003272849,0.0003317423,0.0001230274,0.0001985784,0.00008433079,0.0001353968,0.0002404963,0.0002685502,0.0009311984],"category_scores_gemma":[0.0002509319,0.0001505075,0.0001486086,0.0001436189,0.0001988755,0.0002489245,0.0001600242,0.0004532682,0.0001307259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002784365,"about_ca_system_score_gemma":0.0002422555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009501305,"about_ca_topic_score_gemma":0.001003862,"domain_scores_codex":[0.9999114,0.0000122929,0.000004144105,0.00001947611,0.00002327484,0.00002933318],"domain_scores_gemma":[0.9998387,0.00004800394,0.00005121687,0.00001713023,0.00002726481,0.00001758164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007931874,0.00008432653,0.000297048,0.00002536115,0.000002551747,0.00001948825,0.00001431562,0.0011848,0.9970447,0.00006712597,0.0000546204,0.001126443],"study_design_scores_gemma":[0.000007789395,0.001528516,0.0035939,0.000003896032,0.00001348239,0.00005804382,0.00002010516,0.01268508,0.9813986,0.0000678092,0.0006164281,0.000006317519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658887,0.0007586325,0.03132131,0.00006831848,0.00002339758,0.00009962839,0.000483526,0.0002526776,0.001103803],"genre_scores_gemma":[0.984871,0.0005287975,0.01305977,0.00004209608,0.00000660562,0.0001649483,0.0002882053,0.00003534761,0.001003288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009501305,"threshold_uncertainty_score":0.003115177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203507674392033,"score_gpt":0.2526543420990325,"score_spread":0.2323035746598292,"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."}}