{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003283059,0.0001398031,0.0002472001,0.0005888007,0.0005919408,0.0008953123,0.0001960583,0.0004416024,0.001425377],"category_scores_gemma":[0.01078817,0.0001888534,0.0003879379,0.0006047808,0.0006026955,0.0005048044,0.0008998578,0.000518842,0.0001312489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007633949,"about_ca_system_score_gemma":0.0009008058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007978848,"about_ca_topic_score_gemma":0.008668488,"domain_scores_codex":[0.9978143,0.001156054,0.000163427,0.0001268509,0.0003696597,0.0003697055],"domain_scores_gemma":[0.990988,0.003405426,0.002631474,0.0001596352,0.001125682,0.00168981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002434276,0.0004155335,0.9666626,0.00005269559,0.00004016588,0.0002553193,0.0225406,0.00005082394,0.0003503548,0.00003347802,0.0004499348,0.008905151],"study_design_scores_gemma":[0.00004630704,0.0006399511,0.9567905,0.00003811835,0.00002665458,0.0002359355,0.04120964,0.0002941405,0.0001528552,0.00001510715,0.0005365628,0.00001430496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995004,0.00003320757,0.00001455802,0.00006793785,0.000002746468,0.000006921232,0.00001600184,8.120092e-7,0.0003574061],"genre_scores_gemma":[0.9997221,0.00004820219,0.00004278798,0.00008453216,0.000003783204,0.000008752867,0.0000236131,6.786115e-7,0.00006553735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007978848,"threshold_uncertainty_score":0.01736265,"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."}}