{"id":"W2528467911","doi":"10.1371/journal.pone.0164095","title":"Optical Coherence Tomography in the UK Biobank Study – Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Moorfields Eye Hospital NHS Foundation Trust; Northwest Regional Development Agency; Queen's University; Queen's University Belfast; University of Southampton; Fight for Sight UK; King's College London; Diabetes UK; University of Leeds; National Institute for Health and Care Research; British Heart Foundation; Wellcome Trust; University of Bristol; Kingston University","keywords":"Optical coherence tomography; Computer science; Segmentation; Artificial intelligence; Population; Biobank; Software; Image segmentation; Computer vision; Medicine; Ophthalmology; Bioinformatics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01078163,0.0007154176,0.0005567017,0.00256283,0.0005742673,0.001507282,0.0009425679,0.0008094254,0.001816786],"category_scores_gemma":[0.02316215,0.0004368652,0.0003442622,0.002131595,0.0003222153,0.0009323992,0.001423379,0.0006590874,0.0008973877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007390231,"about_ca_system_score_gemma":0.0008991945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006004296,"about_ca_topic_score_gemma":0.01168619,"domain_scores_codex":[0.9891979,0.006633557,0.001221344,0.001104998,0.001636665,0.0002055583],"domain_scores_gemma":[0.9828627,0.004565612,0.005400805,0.003272581,0.003261735,0.0006366887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001626295,0.0002254071,0.8914385,0.0003637514,0.0003020991,0.0009695441,0.0008219747,0.0004665959,0.009888666,0.000521352,0.01370102,0.07967472],"study_design_scores_gemma":[0.0002136364,0.0002775338,0.9803194,0.0001881948,0.0001284376,0.001367138,0.0002406466,0.001857841,0.003090751,0.0004402042,0.0118284,0.0000478016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9212565,0.006422714,0.04634602,0.002614277,0.000316138,0.002508622,0.01091477,0.0006614022,0.008959532],"genre_scores_gemma":[0.9155664,0.001057383,0.07443047,0.0008152123,0.0004073409,0.002158518,0.004195156,0.000110677,0.001258917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01078163,"threshold_uncertainty_score":0.05701941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04783807735211091,"score_gpt":0.2878219458844443,"score_spread":0.2399838685323334,"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."}}