{"id":"W2339764600","doi":"10.1364/cancer.2016.jtu3a.47","title":"Clinical swept-source optical coherence tomography of the middle ear","year":2016,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Optical coherence tomography; Middle ear; Otology; Optical tomography; Optical imaging; Tomography; Medical imaging; Optics; Coherence (philosophical gambling strategy); Preclinical imaging; Computer science; Biomedical engineering; Medicine; Anatomy; Physics; Surgery; Artificial intelligence; In vivo","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":[0.0002070398,0.0001293536,0.0001873574,0.00004981782,0.00003954933,0.00001219524,0.0004871514,0.0001209372,0.0005078618],"category_scores_gemma":[0.00008454789,0.00006858337,0.0002542233,0.0005375055,0.0004811119,0.00007144962,0.00007671079,0.0001654653,0.000173485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009517447,"about_ca_system_score_gemma":0.00001497531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005082166,"about_ca_topic_score_gemma":0.0000122928,"domain_scores_codex":[0.9988834,0.00003438369,0.0004168271,0.0001977192,0.0002136927,0.0002539506],"domain_scores_gemma":[0.9986872,0.0004356036,0.00003513948,0.000643686,0.00006645413,0.0001319273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003551354,0.0005988596,0.5596923,0.0001412859,0.0003747691,0.00000167829,0.0001823023,0.0003966659,0.05867549,0.1494794,0.01297682,0.2174449],"study_design_scores_gemma":[0.001975518,0.0003757221,0.8346847,0.0004407032,0.0001967327,0.00001363021,0.0002190612,0.00297861,0.0867816,0.01169918,0.05939676,0.001237771],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8614361,0.0001323717,0.05429865,0.0005513008,0.0002619037,0.0004571215,0.00001728225,0.0005116249,0.0823336],"genre_scores_gemma":[0.9941086,0.00002064144,0.005243049,0.00006266686,0.0000497858,0.00004212525,3.277885e-7,0.00002010622,0.0004527698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2749924,"threshold_uncertainty_score":0.5560731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.030861270083939,"score_gpt":0.2556213644809163,"score_spread":0.2247600943969773,"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."}}