{"id":"W4388558226","doi":"10.1097/iae.0000000000003990","title":"OCTess: AN OPTICAL CHARACTER RECOGNITION ALGORITHM FOR AUTOMATED DATA EXTRACTION OF SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY REPORTS","year":2023,"lang":"en","type":"article","venue":"Retina","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; McGill University; McMaster University; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Optical coherence tomography; Optical character recognition; Algorithm; Data set; Test data; Pattern recognition (psychology); Image (mathematics); Medicine","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":[0.0008940824,0.0001445424,0.0003578018,0.0002281772,0.0000621548,0.00003602486,0.0001064389,0.000106834,0.00005303781],"category_scores_gemma":[0.0002964352,0.0001267251,0.0001380355,0.0006027398,0.0001183672,0.0002447175,0.00004332218,0.000182089,0.00002398405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002276647,"about_ca_system_score_gemma":0.00005113807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002313604,"about_ca_topic_score_gemma":0.000001602733,"domain_scores_codex":[0.9983547,0.00004901778,0.0004781375,0.0005020724,0.0003214941,0.00029457],"domain_scores_gemma":[0.9986831,0.0001337855,0.0001883179,0.0006171445,0.0002135807,0.0001640885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007102054,0.001469102,0.01852896,0.0006451933,0.0005878681,0.002365564,0.0003029663,0.000004722142,0.5744642,0.00009923149,0.0123188,0.3885032],"study_design_scores_gemma":[0.002860506,0.002342707,0.4009397,0.001573739,0.002277004,0.003892644,0.001069507,0.448429,0.1299007,0.00201995,0.003685525,0.001008983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901738,0.00002796607,0.006733635,0.0009152164,0.0001683005,0.0003935195,0.0000986178,0.0007599795,0.000728988],"genre_scores_gemma":[0.8440347,0.00005546513,0.1494008,0.00006990026,0.0004853639,0.00004422523,0.005681233,0.00004163312,0.0001866639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4484243,"threshold_uncertainty_score":0.5167698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05018854134322681,"score_gpt":0.3561793532892923,"score_spread":0.3059908119460655,"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."}}