Lack of Evidence to Guide Deprescribing of Antihyperglycemics: A Systematic Review
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Bibliographic record
Abstract
INTRODUCTION: Individualizing glycemic targets to goals of care and time to benefit in persons with type 2 diabetes is good practice, particularly in populations at risk of hypoglycemia and adverse outcomes relating to the use of antihyperglycemics. Guidelines acknowledge the need for relaxed targets in frail older adults, but there is little guidance on how to safely deprescribe (i.e. stop, reduce or substitute) antihyperglycemics. METHODS: The purpose of this study was to synthesize evidence from all studies evaluating the effects of deprescribing versus continuing antihyperglycemics in older adults with type 2 diabetes. To this end, we searched MEDLINE, EMBASE, and Cochrane Library (July 2015) for controlled studies evaluating the effects of deprescribing antihyperglycemics in adults with type 2 diabetes. All such studies were eligible for inclusion in our study, and two independent reviewers screened titles, abstracts and full-text articles, extracted data, and evaluated risk of bias. Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessment and a narrative summary were completed. RESULTS: We identified two controlled before-and-after studies, both of very low quality. One study found that an educational intervention decreased glyburide use while not compromising glucose control. The other reported that cessation of antihyperglycemics in elderly nursing home patients resulted in a non-significant increase in glycated hemoglobin (HbA1C). No significant change in hypoglycemia rate was found in the only study with this outcome measure. CONCLUSIONS: There is limited evidence available regarding deprescribing antihyperglycemic medications. Adequately powered, high-quality studies, particularly in the elderly and with clinically important outcomes, are required to support evidence-based decision-making. PROTOCOL REGISTRATION NUMBER: CRD42015017748.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it