{"id":"W2054335978","doi":"10.1186/1471-2415-13-40","title":"Tear fluid proteomics multimarkers for diabetic retinopathy screening","year":2013,"lang":"en","type":"article","venue":"BMC Ophthalmology","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"European Social Fund; Peterborough K. M. Hunter Charitable Foundation; Moorfields Eye Hospital NHS Foundation Trust; Hungarian Scientific Research Fund; Nemzeti Kutatási és Technológiai Hivatal; European Commission; Canadian Institutes of Health Research; National Institute for Health and Care Research; Ontario Ministry of Health and Long-Term Care","keywords":"Medicine; Diabetic retinopathy; Machine learning; Random forest; Artificial intelligence; Naive Bayes classifier; Support vector machine; Logistic regression; Biomarker; Parameterized complexity; Algorithm; Computer science; Diabetes mellitus","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003112127,0.0009275648,0.0009742851,0.002231129,0.0003840632,0.001240973,0.001035037,0.001310181,0.002620147],"category_scores_gemma":[0.003113343,0.0004024856,0.0005548706,0.001136297,0.0002519385,0.0008977845,0.0007923941,0.0008916593,0.00145128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000499551,"about_ca_system_score_gemma":0.000564197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005469692,"about_ca_topic_score_gemma":0.0008941048,"domain_scores_codex":[0.9984906,0.0004924157,0.0001005941,0.0003420397,0.0004963555,0.00007804393],"domain_scores_gemma":[0.9982622,0.0006603784,0.0002705696,0.0002008955,0.0004997974,0.0001060444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002288347,0.0008626693,0.05597043,0.0008730767,0.0002581397,0.0002853212,0.0001319213,0.004149468,0.3935796,0.001231691,0.004482099,0.5358871],"study_design_scores_gemma":[0.0003544125,0.003628289,0.08869683,0.0003253376,0.0006499412,0.004158959,0.0002727745,0.3023665,0.5752066,0.00533773,0.01876776,0.0002347679],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4234668,0.0194984,0.5396424,0.002419191,0.0005554701,0.001110768,0.002569994,0.006025972,0.004710984],"genre_scores_gemma":[0.5126466,0.00274181,0.4798229,0.000627475,0.000239988,0.0004784845,0.0009957359,0.0001192,0.002327796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003112127,"threshold_uncertainty_score":0.01645869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03568209746015855,"score_gpt":0.30753568194896,"score_spread":0.2718535844888015,"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."}}