{"id":"W2039184942","doi":"10.2196/mhealth.3672","title":"Exploring the Far Side of Mobile Health: Information Security and Privacy of Mobile Health Apps on iOS and Android","year":2015,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":262,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universität zu Köln","keywords":"mHealth; Android (operating system); Internet privacy; Computer science; Private information retrieval; Information privacy; Mobile device; Computer security; World Wide Web; Health care","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":[0.005652919,0.0004336276,0.001315935,0.0004344486,0.001711967,0.0000195267,0.0002717139,0.000232587,0.00001271946],"category_scores_gemma":[0.0002785406,0.0003416952,0.00006184928,0.0006801269,0.0003435943,0.0005814456,0.0002449649,0.001282064,0.00001859363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004302991,"about_ca_system_score_gemma":0.00478877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005291645,"about_ca_topic_score_gemma":0.0005334347,"domain_scores_codex":[0.9928733,0.001255871,0.003046418,0.0006085971,0.0006753168,0.001540565],"domain_scores_gemma":[0.9926221,0.001022305,0.002455463,0.0008827804,0.0004112878,0.002606033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001189662,0.000640446,0.01533856,0.04009479,0.00003309523,7.833345e-7,0.112563,0.00004384737,0.00000872653,0.02568748,0.01214301,0.7922566],"study_design_scores_gemma":[0.009605689,0.01626918,0.1233717,0.002352237,0.00006785491,0.0000539167,0.07207197,0.001249508,0.00003662804,0.003944099,0.7701715,0.0008056861],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574578,0.01372629,0.0001642948,0.009336641,0.0005535721,0.01750547,0.0003878213,0.0001466668,0.0007214053],"genre_scores_gemma":[0.9216418,0.04929798,0.0004637808,0.009763139,0.0002318439,0.01841307,0.0001145359,0.00004442909,0.0000294701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7914509,"threshold_uncertainty_score":0.9999035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1644780972736984,"score_gpt":0.448316514839504,"score_spread":0.2838384175658056,"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."}}