The association between diuretics and falls in older adults: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Diuretic intake increases the risk of falling. However, previous studies have shown inconsistent correlations between diuretics and falls. This meta-analysis aimed to provide a comprehensive overview of the relationship between diuretic use and risk of falls in older adult individuals. METHODS: Six databases (Cochrane Library, PubMed, Medline, CINAHL, Web of Science, and EMBASE) were searched from their inception to November 9, 2022. The risk of bias was independently evaluated using the Newcastle-Ottawa Quality Assessment Scale. A comprehensive meta-analysis was used to analyze the eligible studies. RESULTS: Fifteen articles were analyzed. Studies have shown that diuretics can increase the risk of falls in older adult individuals. The probability of falls in older adult individuals who used diuretics was 1.185 times higher than in those who did not take diuretics. CONCLUSION: Diuretics were significantly associated with an increased risk of falls.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".