{"id":"W3005326200","doi":"10.2196/14396","title":"Health App Use and Its Correlates Among Individuals With and Without Type 2 Diabetes: Nationwide Population-Based Survey","year":2020,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Logistic regression; Type 2 diabetes; Population; Odds; Gerontology; Odds ratio; Diabetes mellitus; Population health; Health Survey for England; Demography; Environmental health","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":[0.0009796944,0.0002758383,0.0005694285,0.0001413736,0.001058717,0.00004891211,0.000121137,0.0002243321,0.00006083141],"category_scores_gemma":[0.0009263285,0.0002359941,0.00002056304,0.000617945,0.0000776261,0.0003208567,0.00006830601,0.0005882683,0.00005262931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006988073,"about_ca_system_score_gemma":0.0006142247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004467186,"about_ca_topic_score_gemma":0.001086163,"domain_scores_codex":[0.9967989,0.0007725388,0.0007352532,0.0005680891,0.0002912516,0.0008339075],"domain_scores_gemma":[0.996008,0.001828844,0.0005616238,0.0002576977,0.0003263976,0.001017418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002617349,0.00003387733,0.9943029,0.0009004024,0.00001932967,7.24303e-8,0.0004679765,0.00002145812,0.000007773837,0.0002318354,0.002763408,0.001224808],"study_design_scores_gemma":[0.001117919,0.00027413,0.9887869,0.000320812,0.00001724935,5.646285e-8,0.00007138383,0.003951829,0.000006829831,0.00005148196,0.005182665,0.0002187134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873906,0.001167081,0.00001985928,0.005915212,0.0001041795,0.004842778,0.0002688633,0.0002475471,0.00004392293],"genre_scores_gemma":[0.9822888,0.0002157137,0.0004259937,0.01296935,0.0001133103,0.003137565,0.0006955241,0.0000612048,0.00009250343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00705414,"threshold_uncertainty_score":0.9623561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05583759151958772,"score_gpt":0.3743730865543817,"score_spread":0.3185354950347939,"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."}}