Authors' reply: Systematic review and meta-analysis of the effects of statin therapy on abdominal aortic aneurysms (<i>Br J Surg</i> 2011; 98: 362–353)
Bibliographic record
Abstract
Sir We would like to thank Bailey and colleagues for their interesting comments. Meta-analysis is only as good as the studies involved. When examining statins and abdominal aortic aneurysm (AAA), all available studies were retrospective. Results were therefore subject to the usual statistical constraints. Newcastle–Ottawa scoring, random-effects models and funnel plots were all used in an attempt to offset these constraints1. As mentioned, the effect of meta-analysing different methods of growth expansion measurement is unclear. However, the results from the large, high-quality studies were so similar (Fig. 2) that even re-analysing the primary data for consistency was unlikely to lead to a different outcome. Medication compliance is one of a number of confounding factors in clinical studies. It is a complex issue that is notoriously difficult to measure reliably, even in randomized controlled trials2,3. The Newcastle–Ottawa score includes up to two stars for adjustment for confounding factors, which were achieved by all of the high-quality studies included in the AAA expansion analysis. Adjusting for factors with a known impact on vascular disease is arguably more important than attempting to control for the nebulous effect of medication compliance, and for this reason it was not addressed.
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 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 | 0.024 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".