{"id":"W3129088867","doi":"10.1097/aud.0000000000000993","title":"Predicting Depression From Hearing Loss Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"Ear and Hearing","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Confidence interval; Hearing loss; Patient Health Questionnaire; Depression (economics); Medicine; National Health and Nutrition Examination Survey; Tinnitus; Audiology; Scale (ratio); Machine learning; Psychiatry; Population; Computer science; Depressive symptoms; Anxiety","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":[0.002752899,0.000863148,0.0006451919,0.001290581,0.0002399551,0.0008437225,0.0006117282,0.0005969905,0.000910724],"category_scores_gemma":[0.009717617,0.0002143291,0.0008585852,0.0005349957,0.0002558751,0.0004653747,0.0005412974,0.001082696,0.0004108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005495648,"about_ca_system_score_gemma":0.0006754211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00510495,"about_ca_topic_score_gemma":0.003757011,"domain_scores_codex":[0.9990345,0.0003767743,0.000111733,0.0002304115,0.0001741981,0.00007240698],"domain_scores_gemma":[0.9948578,0.003732364,0.0004869729,0.0002246509,0.0005717279,0.0001264986],"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":[0.000664317,0.0009619216,0.5166413,0.0001702095,0.000841654,0.0001927719,0.00009071054,0.2600489,0.001555611,0.0002720881,0.003483924,0.2150767],"study_design_scores_gemma":[0.0000502794,0.0002683165,0.06468557,0.00007240965,0.00009345471,0.0001215083,0.00004383268,0.931737,0.001045666,0.001493923,0.0003661647,0.00002193293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.917118,0.002011699,0.07486644,0.001283444,0.00009972495,0.0001979071,0.002071118,0.0005609416,0.001790683],"genre_scores_gemma":[0.9856464,0.0002616912,0.01198098,0.0001424001,0.00005344666,0.00006814882,0.001543399,0.000007810667,0.000295608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00510495,"threshold_uncertainty_score":0.01455891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05499784815968323,"score_gpt":0.3013848785956261,"score_spread":0.2463870304359429,"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."}}