Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections
Bibliographic record
Abstract
Letters3 April 2018Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin InjectionsRoy W. Beck, MD, PhD and Tonya D. Riddlesworth, PhDRoy W. Beck, MD, PhDJaeb Center for Health Research, Tampa, Florida (R.W.B., T.D.R.) and Tonya D. Riddlesworth, PhDJaeb Center for Health Research, Tampa, Florida (R.W.B., T.D.R.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L17-0706 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:The treatment group difference used to calculate a sample size is the postulated estimate of the true population value and is not intended to indicate what would be considered clinically significant, which is a common misconception. The upper limit of the 95% CI for the observed treatment group difference in HbA1c levels was 0.6%, extending beyond the projected true population value of 0.4%. We believe that a treatment group difference of 0.3% represents a meaningful shift in the HbA1c distributions, as evidenced by our finding that 73% of the CGM group achieved an HbA1c reduction of 0.5% or ...References1. Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, Rayman G. Flash glucose-sensing technology as a replacement for blood glucose monitoring for the management of insulin-treated type 2 diabetes: a multicenter, open-label randomized controlled trial. Diabetes Ther. 2017;8:55-73. [PMID: 28000140] doi:10.1007/s13300-016-0223-6 CrossrefMedlineGoogle Scholar2. U.S. Food and Drug Administration. Summary of safety and effectiveness data: Freestyle Libre Pro Flash Glucose Monitoring System. 2016. Accessed at www.accessdata.fda.gov/cdrh_docs/pdf15/p150021b.pdf on 28 October 2017. Google Scholar Author, Article, and Disclosure InformationAffiliations: Jaeb Center for Health Research, Tampa, Florida (R.W.B., T.D.R.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M16-2855. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoContinuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections Roy W. Beck , Tonya D. Riddlesworth , Katrina Ruedy , Andrew Ahmann , Stacie Haller , Davida Kruger , Janet B. McGill , William Polonsky , David Price , Stephen Aronoff , Ronnie Aronson , Elena Toschi , Craig Kollman , Richard Bergenstal , and Continuous Glucose Monitoring Versus Usual Care in Patients With Type 2 Diabetes Receiving Multiple Daily Insulin Injections Thomas Haak Metrics Cited byIMpact of flash glucose Monitoring in pEople with type 2 Diabetes Inadequately controlled with non‐insulin Antihyperglycaemic ThErapy ( IMMEDIATE ): A randomized controlled trialAcceptance and Effect of Continuous Glucose Monitoring on Discharge From Hospital in Patients With Type 2 Diabetes: Open-label, Prospective, Controlled StudyEpisodic Real-Time CGM Use in Adults with Type 2 Diabetes: Results of a Pilot Randomized Controlled TrialPatients with Type 2 Diabetes and Residual Insulin Secretory Capacity Realize Glycemic Benefits from Real-Time Continuous Glucose MonitoringChange in Hemoglobin A1c and Quality of Life with Real-Time Continuous Glucose Monitoring Use by People with Insulin-Treated Diabetes in the Landmark StudyImproved Real-World Glycemic Control With Continuous Glucose Monitoring System Predictive AlertsDiabetes in ageing: pathways for developing the evidence base for clinical guidanceContinuous Glucose Monitoring: Review of an Innovation in Diabetes ManagementGoing beyond HbA1c to understand the benefits of advanced diabetes therapiesDigitale Tools und strukturierte Prozesse verbessern Therapiequalität 3 April 2018Volume 168, Issue 7Page: 526-527KeywordsBody weightDisclosureGlucoseHbA1cHypoglycemiaInsulinPatientsType 2 diabetesWeight gain ePublished: 3 April 2018 Issue Published: 3 April 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".