{"id":"W4360600498","doi":"10.2196/40272","title":"Secure Messaging for Diabetes Management: Content Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Office of Research and Development; AcademyHealth; U.S. Department of Veterans Affairs","keywords":"Glycemic; Diabetes mellitus; Medicine; Diabetes management; Type 2 diabetes; Content analysis; Control (management); Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01265306,0.0004116473,0.0005798184,0.01071912,0.0009955325,0.002189117,0.0005982994,0.0003905748,0.003337059],"category_scores_gemma":[0.05938452,0.0003201798,0.001276216,0.01134966,0.0006216949,0.002015688,0.002547223,0.0006813824,0.0006075962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003167909,"about_ca_system_score_gemma":0.003489989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003259515,"about_ca_topic_score_gemma":0.003866773,"domain_scores_codex":[0.9911913,0.004636489,0.001347901,0.0006186417,0.001835221,0.0003704418],"domain_scores_gemma":[0.9353301,0.0473647,0.007694537,0.001381491,0.00747019,0.0007590785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001111141,0.0009727994,0.4346978,0.01168131,0.0007396081,0.0004771141,0.06126472,0.0009393431,0.00294371,0.00377594,0.02512197,0.4562745],"study_design_scores_gemma":[0.0002896209,0.0007904173,0.8632823,0.005657443,0.001286866,0.0006097407,0.05318716,0.01224668,0.003296304,0.005149393,0.0539789,0.0002253012],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401621,0.002296976,0.0123686,0.001592715,0.0001091185,0.01126372,0.0216493,0.0003526562,0.01020489],"genre_scores_gemma":[0.9079109,0.002820061,0.05716232,0.0005162428,0.0001366296,0.01569049,0.01309905,0.0001529456,0.00251143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01265306,"threshold_uncertainty_score":0.06691653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08956920420553947,"score_gpt":0.4377886028939263,"score_spread":0.3482193986883869,"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."}}