{"id":"W2782609612","doi":"10.2196/diabetes.9143","title":"Health Care Professionals’ Clinical Perspectives on Glycemic Control and Satisfaction With a New Blood Glucose Meter With a Color Range Indicator: Online Evaluation in India, Russia, China, and the United States","year":2018,"lang":"en","type":"article","venue":"JMIR Diabetes","topic":"Hyperglycemia and glycemic control in critically ill and hospitalized patients","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Glycemic; Glucose meter; Diabetes mellitus; Medicine; China; Control (management); Target range; Gerontology; Endocrinology; Computer science; Geography; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0004828985,0.0002416816,0.0006235167,0.0002162013,0.0001782851,0.00003498064,0.00005565622,0.0001397837,0.00003213811],"category_scores_gemma":[0.0001835516,0.0001271844,0.00004631862,0.0002455753,0.0006226105,0.00008970976,0.00002457099,0.0004348272,0.000002498368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008406843,"about_ca_system_score_gemma":0.0003498426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005309379,"about_ca_topic_score_gemma":0.0005493122,"domain_scores_codex":[0.9979977,0.0003707181,0.0004064564,0.0004467284,0.0004415291,0.0003368411],"domain_scores_gemma":[0.9986369,0.0005173209,0.0001841998,0.0002208929,0.0001910885,0.0002495926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02253535,0.0006500849,0.9421306,0.0001191709,0.0003541443,0.000005550503,0.02064931,9.573048e-7,0.00002571377,0.0001829659,0.0001076079,0.01323861],"study_design_scores_gemma":[0.06862142,0.01233003,0.9088452,0.0006910648,0.0004290018,0.000009914888,0.007502747,0.001191111,0.00001685593,0.00008871966,0.00008944434,0.0001845201],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879743,0.00274929,0.000005813923,0.006390058,0.00004946948,0.002696647,0.0000593299,0.00003208369,0.00004303808],"genre_scores_gemma":[0.995627,0.0002421815,0.0002008195,0.00327094,0.0001879383,0.0002915022,0.000139672,0.00002489016,0.00001503731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04608607,"threshold_uncertainty_score":0.5186431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170449786417018,"score_gpt":0.3297977059875843,"score_spread":0.3180932081234142,"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."}}