Effects of study design and trends for EVAR versus OSR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To investigate if study design factors such as randomization, multi-center versus single center evidence, institutional surgical volume, and patient selection affect the outcomes for endovascular repair (EVAR) versus open surgical repair (OSR). Finally, we investigate trends over time in EVAR versus OSR outcomes. METHODS: Search strategies for comparative studies were performed individually for: OVID's MEDLINE, EMBASE, CINAHL, HAPI, and Evidence Based Medicine (EBM) Reviews (including Cochrane DSR, ACP Journal Club, DARE and CCTR), limited to 1990 and November 2006. RESULTS: Identified literature: 84 comparative studies pertaining to 57,645 patients. These include 4 randomized controlled trials (RCTs), plus 2 RCTs with long-term follow-up. The other 78 comparative studies were nonrandomized with 75 reporting perioperative outcomes, of which 16 were multi-center, and 59 single-center studies. Of the single-center studies 31 were low-volume and 28 were high-volume centers. In addition, 5 studies had all patients anatomically eligible for EVAR, and 8 studies included high-risk patients only. Finally, 25 long term observational studies reported outcomes up to 3 years. OUTCOMES: Lower perioperative mortality and rates of complications for EVAR versus OSR varied across study designs and patient populations. EVAR adverse outcomes have decreased in recent times. CONCLUSION: EVAR highlights the problem of performing meta-analysis when the experience evolves over time.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it