{"id":"W2336692385","doi":"10.1364/cancer.2016.jth3a.3","title":"Multi-excitation, Multi-emission Autofluorescence Imaging (AFI) for the In Vivo Identification of at Risk Cervical Tissue","year":2016,"lang":"en","type":"article","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Occupational Cancer Research Centre","funders":"","keywords":"Autofluorescence; Medicine; Confounding; In vivo; Excitation; Cervix; Pathology; Optics; Fluorescence; Cancer; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001239056,0.0004377419,0.0004091109,0.001328945,0.0002956477,0.0006471204,0.0004383259,0.001166171,0.002009249],"category_scores_gemma":[0.001206015,0.0004456456,0.0003265665,0.0004489049,0.0003258637,0.0008303208,0.0003951845,0.0007730559,0.0004311526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949821,"about_ca_system_score_gemma":0.0003758295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008951831,"about_ca_topic_score_gemma":0.00221102,"domain_scores_codex":[0.9997215,0.0001008295,0.00001455093,0.00005277937,0.00007514872,0.00003519786],"domain_scores_gemma":[0.999404,0.0003221792,0.00008610766,0.00004047598,0.0000926607,0.00005457881],"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":[0.0003614253,0.0000789614,0.00869367,0.0004286469,0.00005098005,0.0004147415,0.00006090392,0.00067088,0.9325604,0.0004868618,0.0005872229,0.05560523],"study_design_scores_gemma":[0.00004426885,0.0007148726,0.05925474,0.000206696,0.0001911578,0.006701397,0.0002846351,0.02387234,0.9006712,0.001199989,0.00676719,0.00009171922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6663857,0.04805736,0.2696872,0.001848881,0.0001885198,0.0002707565,0.0008965154,0.001139374,0.0115257],"genre_scores_gemma":[0.8023243,0.01146227,0.1812368,0.0005980034,0.00008433791,0.0001610764,0.0003139657,0.0001315268,0.003687692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002009249,"threshold_uncertainty_score":0.006721616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065286369670286,"score_gpt":0.2540566313592494,"score_spread":0.2434037676625465,"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."}}