{"id":"W4205404706","doi":"10.2196/27220","title":"Use of Health Information Technology by Adults With Diabetes in the United States: Cross-sectional Analysis of National Health Interview Survey Data (2016-2018)","year":2022,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"eHealth; Medicine; National Health Interview Survey; Diabetes mellitus; Health Information National Trends Survey; Family medicine; Health care; Gerontology; Obesity; Type 2 diabetes; Ethnic group; Community health; Cross-sectional study; Health information; Public health; Environmental health; Nursing; Internal medicine; Population; Endocrinology","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":[0.01094571,0.0001952201,0.0007140564,0.001614076,0.0009360624,0.00001645974,0.0008690929,0.000122065,0.0002141742],"category_scores_gemma":[0.0004747442,0.0001518947,0.00005419584,0.006143077,0.0001963594,0.0004312757,0.0003678295,0.0008010098,0.000008448525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004531261,"about_ca_system_score_gemma":0.002184635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00523774,"about_ca_topic_score_gemma":0.003462617,"domain_scores_codex":[0.9922046,0.003494945,0.00231469,0.0003967244,0.0007977762,0.0007912399],"domain_scores_gemma":[0.9936863,0.002302119,0.002234642,0.0009726295,0.0006565826,0.0001477194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004441906,0.0002911691,0.9636844,0.001092675,0.0001122175,1.839498e-8,0.001451554,0.0007943018,0.000001029916,0.0002856248,0.02836059,0.00388202],"study_design_scores_gemma":[0.0007832989,0.0004266454,0.9115276,0.0001533546,0.000012959,9.955125e-8,0.00206675,0.009620609,0.000001041338,0.00008889008,0.07520503,0.00011376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679505,0.000941766,0.0002612004,0.005750643,0.0001211445,0.004501299,0.02038334,0.00006664054,0.0000235092],"genre_scores_gemma":[0.9212748,0.0005223237,0.000228746,0.009660426,0.00001656344,0.008318944,0.05993085,0.00001949668,0.00002778407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05215681,"threshold_uncertainty_score":0.791793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114756740485684,"score_gpt":0.4334084780935808,"score_spread":0.3186517376078968,"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."}}