The Efficacy of Pharmacotherapy for Decreasing the Expansion Rate of Abdominal Aortic Aneurysms: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Pharmacotherapy may represent a potential means to limit the expansion rate of abdominal aortic aneurysms (AAAs). Studies evaluating the efficacy of different pharmacological agents to slow down human AAA-expansion rates have been performed, but they have never been systematically reviewed or summarized. METHODS AND FINDINGS: Two independent reviewers identified studies and selected randomized trials and prospective cohort studies comparing the growth rate of AAA in patients with pharmacotherapy vs. no pharmacotherapy. We extracted information on study interventions, baseline characteristics, methodological quality, and AAA growth rate differences (in mm/year). Fourteen prospective studies met eligibility criteria. Five cohort studies raised the possibility of benefit of beta-blockers [pooled growth rate difference: -0.62 mm/year, (95%CI, -1.00 to -0.24)], but this was not confirmed in three beta-blocker RCTs [pooled RCT growth rate difference: -0.05 mm/year (-0.16 to 0.05)]. Statins have been evaluated in two cohort studies that yield a pooled growth rate difference of -2.97 (-5.83 to -0.11). Doxycycline and roxithromycin have been evaluated in two RCTs that suggest possible benefit [pooled RCT growth rate difference: -1.32 mm/year (-2.89 to 0.25)]. Studies assessing NSAIDs, diuretics, calcium channel blockers and ACE inhibitors, meanwhile, did not find statistically significant differences. CONCLUSIONS: Beta-blockers do not appear to significantly reduce the growth rate of AAAs. Statins and other anti-inflammatory agents appear to hold promise for decreasing the expansion rate of AAA, but need further evaluation before definitive recommendations can be made.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".