{"id":"W2169539709","doi":"10.1109/cbms.2005.36","title":"Automated Optic Nerve Analysis for Diagnostic Support in Glaucoma","year":2005,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Nova Scotia Health Research Foundation","keywords":"Glaucoma; Optic nerve; Computer science; Artificial intelligence; Computer vision; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Tomography; Confocal; Feature selection; Radiology; Ophthalmology; Medicine; Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002327738,0.0001140242,0.0004164949,0.0004823173,0.0000278676,0.00002279073,0.00006379357,0.00004380675,0.0005463554],"category_scores_gemma":[0.0004015458,0.00008912063,0.0003025885,0.001051765,0.00002709584,0.00006142226,0.00001433849,0.00007194733,0.00009399551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005947152,"about_ca_system_score_gemma":0.00003856543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003121088,"about_ca_topic_score_gemma":0.0001649507,"domain_scores_codex":[0.99902,0.00001919841,0.0003203914,0.0002359188,0.0001502682,0.0002541996],"domain_scores_gemma":[0.9992424,0.0002807976,0.00004765925,0.0002355953,0.00007908151,0.0001145205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003917018,0.0002617467,0.9877052,0.00003515414,0.0007388352,0.0001646454,0.00009577061,0.0005808932,0.0004862924,0.00009262568,0.006848902,0.002950766],"study_design_scores_gemma":[0.001071303,0.0001266397,0.5548566,0.00002557831,0.002703725,0.00002954159,0.00008442711,0.4381891,0.0009299624,0.0000152572,0.00182553,0.0001423222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885914,0.00008409303,0.003493104,0.004471286,0.00001493229,0.0001872837,0.000005071696,0.0002720399,0.002880736],"genre_scores_gemma":[0.9799176,0.00001028329,0.01349902,0.0004096789,0.00007327866,0.00003025397,0.0001451554,0.00001264941,0.005902096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4376082,"threshold_uncertainty_score":0.5982209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322010129232295,"score_gpt":0.3099614219864457,"score_spread":0.2967413206941227,"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."}}