{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000202243,0.0001117461,0.000159211,0.00005164342,0.0005796138,0.0001479349,0.00005397322,0.00007478346,0.00005545081],"category_scores_gemma":[0.0006574203,0.0001072951,0.00004493553,0.0001536485,0.00004893052,0.0002142797,0.0002058398,0.0003790373,0.00001032142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003182064,"about_ca_system_score_gemma":0.00003486527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001247971,"about_ca_topic_score_gemma":0.000020813,"domain_scores_codex":[0.9987432,0.0001447303,0.0001970372,0.0004482466,0.0002018609,0.000264923],"domain_scores_gemma":[0.999382,0.0002915011,0.00004723417,0.0001477527,0.0000252116,0.0001062715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006252223,0.00001242849,0.4336587,0.00002133338,0.000002014501,0.00002707715,0.0006555764,0.0007967045,0.5620115,0.0000440638,3.670492e-7,0.002763904],"study_design_scores_gemma":[0.0005227809,0.00003414277,0.5681719,0.000366467,0.00001566995,0.0000947522,0.0002868178,0.1712473,0.2582111,0.0004156468,0.000394497,0.0002389622],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977453,0.0004431372,0.0007208747,0.0001037272,0.0001986271,0.00006450024,0.000003042734,0.0001064808,0.0006142689],"genre_scores_gemma":[0.9969271,0.00006598491,0.002544713,0.00009063922,0.0001661697,0.000001836692,0.000002841833,0.000023431,0.0001772725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3038005,"threshold_uncertainty_score":0.4457979,"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."}}