{"id":"W7133646987","doi":"10.5281/zenodo.18870623","title":"Mobile Health Monitoring Systems for Diabetes Management among Urban Youth in Nairobi, 2008","year":2008,"lang":"en","type":"article","venue":"Open MIND","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Software deployment; mHealth; Public health; Mobile apps; Diabetes management; Mobile technology; Health care; Diabetes mellitus; Focus group","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001271063,0.0004265164,0.0003357308,0.0005572767,0.001088519,0.001063494,0.0006349322,0.0004552818,0.002093494],"category_scores_gemma":[0.003059689,0.0003700972,0.0004156413,0.001147616,0.0002984005,0.0006727448,0.0009337465,0.0004251056,0.0002628401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002491747,"about_ca_system_score_gemma":0.004122477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3213366,"about_ca_topic_score_gemma":0.4913686,"domain_scores_codex":[0.9994205,0.000261288,0.00002845516,0.00005966992,0.00008435714,0.0001457252],"domain_scores_gemma":[0.9995986,0.0001288149,0.0001085116,0.00001359211,0.0001001122,0.00005039255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002017633,0.0008273772,0.9196945,0.0003446861,0.0001365055,0.0004158989,0.004230838,0.004399279,0.0008649683,0.001182479,0.00150512,0.06619656],"study_design_scores_gemma":[0.0001608087,0.0009586806,0.9197496,0.0004925146,0.0003924123,0.000338297,0.01832382,0.04954927,0.001056312,0.0004302776,0.008503656,0.00004438854],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935102,0.0005735084,0.001576741,0.0006279075,0.00001322444,0.0004675283,0.0006017728,0.00001906966,0.002609988],"genre_scores_gemma":[0.9942806,0.000600177,0.003141741,0.00008061616,0.000005192346,0.0002586662,0.0002822596,0.000002543678,0.001348122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3213366,"threshold_uncertainty_score":0.6389323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080392402857788,"score_gpt":0.4254230056593336,"score_spread":0.3173837653735547,"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."}}