Lack of Evidence to Guide Deprescribing of Antihyperglycemics: A Systematic Review
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".