{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001230906,0.0009366669,0.0005028514,0.002932052,0.0003026746,0.0008280405,0.0007796884,0.0005046466,0.001839569],"category_scores_gemma":[0.004798898,0.0002602026,0.0007462427,0.001067161,0.0003261166,0.0009854485,0.000879413,0.0005710429,0.001336252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007196691,"about_ca_system_score_gemma":0.0008642343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002775134,"about_ca_topic_score_gemma":0.003612229,"domain_scores_codex":[0.9990884,0.0001289938,0.0001608327,0.0002521648,0.0003223703,0.0000472052],"domain_scores_gemma":[0.9977411,0.0006647563,0.0004806874,0.0002536047,0.0007917625,0.00006816186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003930571,0.0001513386,0.01227303,0.0002442482,0.000142466,0.0002205639,0.0001213609,0.01637929,0.03877541,0.001121957,0.01879814,0.9113792],"study_design_scores_gemma":[0.00007948271,0.0003942333,0.01857863,0.00008530683,0.00009352179,0.001248718,0.0001334916,0.822365,0.1319832,0.002360181,0.02259073,0.00008742307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1064198,0.001099714,0.8582845,0.0003584328,0.0002026165,0.0006911531,0.004777067,0.02552888,0.002637802],"genre_scores_gemma":[0.204105,0.0003687666,0.7820887,0.0002792833,0.00008776468,0.000556485,0.008131578,0.0004367064,0.003945684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002932052,"threshold_uncertainty_score":0.006509781,"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."}}