{"id":"W3215710945","doi":"10.1186/s13040-021-00281-8","title":"Development of glaucoma predictive model and risk factors assessment based on supervised models","year":2021,"lang":"en","type":"article","venue":"BioData Mining","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Glaucoma; Machine learning; Artificial intelligence; Data science; Risk analysis (engineering); Medicine; Ophthalmology","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":[0.0002241643,0.0001307687,0.0002851545,0.0001058381,0.00008384713,0.00002025927,0.00005709231,0.00004216516,0.00002004749],"category_scores_gemma":[0.00007944686,0.0001066768,0.000055785,0.0001531413,0.00003762454,0.00008061558,0.00006185797,0.0001093946,6.245388e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004613966,"about_ca_system_score_gemma":0.0003288353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002534889,"about_ca_topic_score_gemma":0.000004203298,"domain_scores_codex":[0.9989323,0.00004102903,0.0002796404,0.0003086488,0.0002929787,0.0001454047],"domain_scores_gemma":[0.9993305,0.00007702618,0.00009425575,0.0002910867,0.0001014825,0.0001056345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001691212,0.0004803118,0.956747,0.0001852186,0.0005263974,0.00004524691,0.003376534,0.003847864,0.01712128,0.00004675638,0.0002308783,0.0172234],"study_design_scores_gemma":[0.0006819917,0.00006384934,0.06900541,0.0002207633,0.0002439978,0.000001869751,0.00164253,0.9195775,0.008401199,0.00001028882,0.00005201012,0.00009859442],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.943854,0.00007228615,0.05480021,0.00009467551,0.00001613474,0.00006182921,0.00009760877,0.0000231473,0.0009801554],"genre_scores_gemma":[0.8287465,0.00003458258,0.17079,0.00005406063,0.00001021206,0.000004406925,0.0002778971,0.00001040817,0.0000719888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9157296,"threshold_uncertainty_score":0.4350152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509305354175464,"score_gpt":0.3059507429795755,"score_spread":0.2508576894378208,"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."}}