{"id":"W4401595247","doi":"10.2196/59810","title":"Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai University of Traditional Chinese Medicine; Shanghai Municipal Health Commission","keywords":"Preprint; Visual impairment; Psychology; Algorithm; Artificial intelligence; Machine learning; Computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003477815,0.0002258062,0.0003703764,0.0002152651,0.000229281,0.0000217685,0.00003543604,0.00009940961,0.00002229085],"category_scores_gemma":[0.00004711519,0.0001751584,0.00009457643,0.0001994138,0.0001253982,0.0002126602,0.0001107066,0.0004091192,0.000001703895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005795516,"about_ca_system_score_gemma":0.00002895347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001783336,"about_ca_topic_score_gemma":0.000007156872,"domain_scores_codex":[0.9987359,0.00007249179,0.0003618138,0.0003538073,0.0001997632,0.000276216],"domain_scores_gemma":[0.9995739,0.00007379747,0.000115417,0.000103757,0.00003956298,0.00009350455],"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":[0.0001512077,0.0003220044,0.988677,0.001022624,0.0001194427,0.00008952311,0.005602772,0.0003262676,0.000619053,3.24066e-7,0.000007964652,0.00306182],"study_design_scores_gemma":[0.0008065006,0.0005919658,0.4711778,0.001311416,0.0001181765,0.0001129996,0.0003697441,0.5252331,0.0001841242,0.00001203495,7.00132e-7,0.00008143966],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968024,0.001599822,0.0006687828,0.00001669292,0.000175283,0.0005455764,0.00002201694,0.0001405002,0.00002886239],"genre_scores_gemma":[0.9986851,0.0001536986,0.0006636239,0.00001574017,0.0001295108,0.00003825521,0.00001639198,0.00003518043,0.0002625291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5249068,"threshold_uncertainty_score":0.7142755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967001086723873,"score_gpt":0.3481982466887781,"score_spread":0.3285282358215393,"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."}}