{"id":"W2838688296","doi":"10.2196/mhealth.9888","title":"Mobile Apps for Blood Pressure Monitoring: Systematic Search in App Stores and Content Analysis","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Radboud Institute for Health Sciences; Radboud Universitair Medisch Centrum; Radboud Universiteit","keywords":"Android (operating system); Mobile apps; mHealth; App store; Computer science; Android app; World Wide Web; Internet privacy; Medicine; Operating system; Psychological intervention; Nursing","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003717732,0.0003564684,0.001298444,0.0007297425,0.001912487,0.00002684041,0.0002382798,0.0004239741,0.00003986243],"category_scores_gemma":[0.0002106647,0.000307993,0.0000927777,0.001084378,0.0002136493,0.0001310693,0.0001165104,0.0008229637,0.0000278014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535449,"about_ca_system_score_gemma":0.001225485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001902696,"about_ca_topic_score_gemma":0.001067462,"domain_scores_codex":[0.9940515,0.0009137861,0.001901058,0.0009302722,0.000398985,0.001804399],"domain_scores_gemma":[0.9952636,0.001264482,0.0006389172,0.0007229846,0.0004706516,0.001639412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001038903,0.0007836639,0.577741,0.3733984,0.0004659001,0.000003253672,0.01930528,0.00001628931,0.00005737563,0.01273323,0.002430449,0.01202622],"study_design_scores_gemma":[0.01720708,0.006307549,0.8499236,0.01022448,0.006583036,0.00002489201,0.03715673,0.009456125,0.00005920563,0.001250483,0.06032142,0.001485381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940057,0.03036406,0.0006756917,0.001415211,0.0005635335,0.02600061,0.0002377115,0.0002252964,0.0004608827],"genre_scores_gemma":[0.9500108,0.006156339,0.001292644,0.001171966,0.000691182,0.03943162,0.00005327598,0.00005248134,0.001139703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3631739,"threshold_uncertainty_score":0.9999372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1495989215656463,"score_gpt":0.4872128776137224,"score_spread":0.3376139560480761,"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."}}