Deprescribing antihyperglycemic agents in older persons: Evidence-based clinical practice guideline.
Bibliographic record
Abstract
OBJECTIVE: To develop an evidence-based guideline to help clinicians make decisions about when and how to safely taper, stop, or switch antihyperglycemic agents in older adults. METHODS: We focused on the highest level of evidence available and sought input from primary care professionals in guideline development, review, and endorsement processes. Seven clinicians (2 family physicians, 3 pharmacists, 1 nurse practitioner, and 1 endocrinologist) and a methodologist comprised the overall team; members disclosed conflicts of interest. We used a rigorous process, including the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach, for guideline development. We conducted a systematic review to assess evidence for the benefits and harms of deprescribing antihyperglycemic agents. We performed a review of reviews of the harms of continued antihyperglycemic medication use, and narrative syntheses of patient preferences and resource implications. We used these syntheses and GRADE quality-of-evidence ratings to generate recommendations. The team refined guideline content and recommendation wording through consensus and synthesized clinical considerations to address common front-line clinician questions. The draft guideline was distributed to clinicians and stakeholders for review and revisions were made at each stage. A decision-support algorithm was developed to accompany the guideline. RECOMMENDATIONS: We recommend deprescribing antihyperglycemic medications known to contribute to hypoglycemia in older adults at risk or in situations where antihyperglycemic medications might be causing other adverse effects, and individualizing targets and deprescribing accordingly for those who are frail, have dementia, or have a limited life expectancy. CONCLUSION: This guideline provides practical recommendations for making decisions about deprescribing antihyperglycemic agents. Recommendations are meant to assist with, not dictate, decision making in conjunction with patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".