{"id":"W2783252359","doi":"10.2196/mhealth.9236","title":"Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Science Foundation","keywords":"Gestational diabetes; mHealth; Computer science; Recommender system; Software; Diabetes mellitus; Medicine; Pregnancy; Machine learning; Psychological intervention; Gestation","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":[0.001201593,0.0004324074,0.0005383465,0.0003863464,0.0002241922,0.0005128859,0.0008633873,0.0005919303,0.002479509],"category_scores_gemma":[0.003178425,0.0001568567,0.0003217796,0.0001966823,0.0001443905,0.0003913961,0.000547604,0.0004080402,0.0007399021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003389366,"about_ca_system_score_gemma":0.0007445485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002248509,"about_ca_topic_score_gemma":0.001273,"domain_scores_codex":[0.9994721,0.0001900238,0.00005664346,0.0001074389,0.0001213708,0.00005247234],"domain_scores_gemma":[0.9989336,0.0004508473,0.00006079831,0.00008471865,0.000335581,0.0001343904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.008341671,0.005137927,0.1460424,0.001328231,0.0004130175,0.003295283,0.001998753,0.0120117,0.0802447,0.001289122,0.01467855,0.7252186],"study_design_scores_gemma":[0.00320254,0.02568245,0.2672673,0.000716205,0.001640816,0.004914884,0.002977456,0.5153221,0.1307043,0.001369871,0.04587546,0.0003266254],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9147157,0.0003996655,0.07116804,0.0007809645,0.0002168571,0.002300278,0.001101499,0.005911305,0.003405683],"genre_scores_gemma":[0.871277,0.0003761198,0.1223161,0.0004668836,0.0000407334,0.001284132,0.001727779,0.00008612953,0.002425092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002479509,"threshold_uncertainty_score":0.008294821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03898967738404178,"score_gpt":0.3545549508173871,"score_spread":0.3155652734333453,"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."}}