Exploring Drug Re-Purposing for Treatment of Abdominal Aortic Aneurysms: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Large abdominal aortic aneurysms (AAAs) present a significant mortality risk. While numerous medical interventions have been proposed, no drugs have convincingly reduced AAA progression, rupture rates, or repair risk. This systematic review and meta-analysis aimed to assess the impact of re-purposed drugs or dietary supplements on slowing expansion rates, reducing the risk of rupture, or minimising the risk of repair for individuals with AAA. METHODS: A systematic search was conducted in five databases. Both observational studies and randomised controlled trials were included. Unpublished data from two screening trials were incorporated. Risk of bias was assessed using the Newcastle-Ottawa scale and revised Cochrane risk of bias tool. Meta-analyses were performed for each identified drug subclass and were stratified by overall risk of bias. Results were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: > 80%). Both groups had a GRADE certainty of very low. Metformin, excluding high risk of bias studies, presented an estimated mean growth difference of AAA diameter between users and non-users of -0.73 mm/year, whilst statins had an overall estimated mean difference of -0.84 mm/year. CONCLUSION: This systematic review and meta-analysis suggests that metformin and statins may provide some effect in slowing AAA progression. However, no definitive evidence was found for any of the investigated drugs included in this study. Further research is needed to identify effective medical treatments for AAA progression with more robust methodology.
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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".