Impact of frailty on mortality, hospitalization, cardiovascular events, and complications in patients with diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Several studies have focused on the impact of frailty on the health outcomes of individuals with diabetes mellitus (DM). This meta-analysis aims to systematically synthesize the existing evidence on frailty and its association with mortality, hospitalizations, cardiovascular diseases, and diabetic complications in DM. Methods A comprehensive search in PubMed, Embase, and SCOPUS was carried out to identify relevant studies assessing the impact of frailty on mortality, hospitalizations, complications, and cardiovascular events in individuals with DM. The quality of the included studies was evaluated using the New Castle Ottawa Scale. Results From the 22 studies included, our meta-analysis revealed significant associations between frailty and adverse outcomes in individuals with DM. The pooled hazard ratios for mortality and frailty showed a substantial effect size of 1.84 (95% CI 1.46–2.31). Similarly, the odds ratio for hospitalization and frailty demonstrated a significant risk with an effect size of 1.63 (95% CI 1.50–1.78). In addition, frailty was associated with an increased risk of developing diabetic nephropathy (HR, 3.17; 95% CI 1.16–8.68) and diabetic retinopathy (HR, 1.94; 95% CI 0.80–4.71). Conclusion Our results show a consistent link between frailty and increased mortality, heightened hospitalization rates, and higher risks of cardiovascular disease, diabetic nephropathy, and diabetic retinopathy for patients with DM. PROSPERO Registration Number: CRD42023485166
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".