How does deprescribing (not) reduce mortality? A review of a meta‐analysis in community‐dwelling older adults casts uncertainty over claimed benefits
Bibliographic record
Abstract
Some meta-analyses suggest that deprescribing may reduce mortality. Our aim was to determine the underlying factors contributing to this observed reduction. We analysed data from 12 randomized controlled trials included in the latest meta-analysis on deprescribing in community-dwelling older adults. Our analysis focused on deprescribed medications and potential methodological concerns. Only a third (4/12) of the trials aimed to study mortality, and that too as a secondary outcome. Five trials reported a reduction in total medications, potentially inappropriate medications or drug-related problems. Information on specific classes of deprescribed medications was limited, although a wide array was concerned (e.g., antihypertensive, sedative, gastro-intestinal medications and vitamins). Follow-up periods were ≤1 year in 11 trials, and five trials included ≤150 participants. Small sample sizes often resulted in imbalanced groups (e.g., comorbidities, number of potentially inappropriate medications), yet no trials presented multivariable analyses. In the two trials with the most weight in the meta-analysis, several deaths occurred before the intervention, making it difficult to draw conclusions about the impact of the deprescribing intervention on mortality. These methodological issues cast significant uncertainty on the benefits of deprescribing on mortality outcomes. Large-scale, well-designed trials are needed to address this issue effectively.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".