{"id":"W4322752273","doi":"10.2196/35858","title":"Mobile Apps for Dietary and Food Timing Assessment: Evaluation for Use in Clinical Research","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institutes of Health; National Institute of General Medical Sciences; Harvard Catalyst; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Massachusetts General Hospital","keywords":"Usability; Mobile apps; App store; Computer science; Internet privacy; World Wide Web; Medicine; Human–computer interaction","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06262246,0.001417778,0.00176935,0.002051893,0.001144582,0.003157105,0.0014416,0.002103363,0.003859372],"category_scores_gemma":[0.1824228,0.0007870034,0.003041238,0.001205208,0.0008511786,0.002846276,0.00267354,0.001248937,0.0009838439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001595486,"about_ca_system_score_gemma":0.004174419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00126505,"about_ca_topic_score_gemma":0.003139435,"domain_scores_codex":[0.9533803,0.03073554,0.00692637,0.00113133,0.006773071,0.001053393],"domain_scores_gemma":[0.8470715,0.1110136,0.01162288,0.003608378,0.02361325,0.003070452],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0372941,0.01234761,0.0371154,0.03085381,0.001804435,0.000505151,0.007435698,0.001003471,0.003198733,0.0008054192,0.01532868,0.8523076],"study_design_scores_gemma":[0.08141424,0.2935871,0.3251986,0.08250766,0.01922478,0.004321435,0.01822645,0.02595847,0.01949824,0.00464197,0.1235081,0.001912924],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7468469,0.02868373,0.02074306,0.005218661,0.001584039,0.1788968,0.003668528,0.00185643,0.01250181],"genre_scores_gemma":[0.5866084,0.02219999,0.1825048,0.002114577,0.0005441583,0.201338,0.001896847,0.0002232923,0.002570016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9373776,"threshold_uncertainty_score":0.3311832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6998562064075015,"score_gpt":0.7200615977443118,"score_spread":0.02020539133681032,"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."}}