{"id":"W2147244538","doi":"10.1177/193229681100500229","title":"Self-Monitoring Technologies for Type 2 Diabetes and the Prevention of Cardiovascular Complications: Perspectives from End Users","year":2011,"lang":"en","type":"article","venue":"Journal of Diabetes Science and Technology","topic":"Diabetes Management and Education","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute","funders":"Canadian Institutes of Health Research","keywords":"Dieticians; Focus group; Medicine; Qualitative research; Type 2 Diabetes Mellitus; Health care; Self-monitoring; Glucose meter; Nursing; Diabetes mellitus; Medical education; Psychology; Business","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008690584,0.00006968067,0.0002658733,0.0004393051,0.0001131604,0.00001897526,0.0002069391,0.00006113855,0.000001495467],"category_scores_gemma":[0.0006874063,0.00004570689,0.0000605347,0.0006023414,0.001253742,0.0002201811,0.0000763978,0.0001042069,2.748227e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003206612,"about_ca_system_score_gemma":0.00007274957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001863698,"about_ca_topic_score_gemma":1.613403e-7,"domain_scores_codex":[0.9993027,0.00001219817,0.0002044514,0.0001430199,0.0001813716,0.0001561984],"domain_scores_gemma":[0.998875,0.00008722354,0.0002119956,0.00021403,0.0005885384,0.00002326231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005081365,0.0003498439,0.755354,0.000215342,0.001430897,4.843308e-7,0.003467282,0.000001004434,0.0289623,0.01427387,0.00007204282,0.1958221],"study_design_scores_gemma":[0.005864761,0.003493323,0.4145281,0.0008177657,0.003260345,0.000007911896,0.06645548,0.0006923148,0.356881,0.1435738,0.004055716,0.0003695456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808883,0.01715276,0.00003140583,0.001337704,0.0001338011,0.0002724966,7.4812e-7,0.00003776522,0.0001449906],"genre_scores_gemma":[0.9891346,0.001282444,0.009517027,0.000007940123,0.00003015717,0.00001797686,3.431978e-7,0.000004405191,0.000005148504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3408259,"threshold_uncertainty_score":0.4619464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03065114906034053,"score_gpt":0.2803680672769572,"score_spread":0.2497169182166167,"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."}}