{"id":"W2080728698","doi":"10.1109/embc.2012.6347352","title":"Glaucoma risk assessment based on clinical data and automated nerve fiber layer defects detection","year":2012,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Newfoundland and Labrador; Kowa Company","keywords":"Glaucoma; Nerve fiber layer; Computer science; Risk assessment; Artificial intelligence; Artificial neural network; Support vector machine; Optic nerve; Medicine; Optometry; Pattern recognition (psychology); Machine learning; Ophthalmology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070539,0.000106733,0.0002284879,0.0000746768,0.00006867839,0.00002704872,0.00005959288,0.00006793902,0.000220711],"category_scores_gemma":[0.0002747655,0.00007289631,0.00007234428,0.0001382564,0.0000473957,0.0001182924,0.00005577135,0.0002323506,0.00009951736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002334332,"about_ca_system_score_gemma":0.000026428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001500805,"about_ca_topic_score_gemma":0.000009881518,"domain_scores_codex":[0.9989145,0.0001964875,0.0002379173,0.0002599267,0.0002006207,0.0001905081],"domain_scores_gemma":[0.9988673,0.0002498297,0.00008260054,0.0005575306,0.00004520716,0.0001975674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005909166,0.0002644029,0.9744752,0.00001760386,0.0000842767,0.000008553482,0.000006179863,0.000002970092,0.0005524072,0.000002629104,0.00377543,0.02075124],"study_design_scores_gemma":[0.0006888387,0.0001990995,0.6023115,0.00002713495,0.0004319635,0.00001100006,0.00001997815,0.3925508,0.0005460039,0.000002206451,0.003143054,0.00006844001],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908396,0.00005681525,0.00292425,0.0004736349,0.00009251134,0.0001044128,0.000007768665,0.0002721822,0.00522881],"genre_scores_gemma":[0.9931904,0.00001952168,0.005320619,0.0005595775,0.0001906167,0.000003322151,0.00006831822,0.00001341814,0.0006341744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3925478,"threshold_uncertainty_score":0.2972625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06066777130451843,"score_gpt":0.4135578315259764,"score_spread":0.352890060221458,"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."}}