322 Comparative Analysis of Endovascular Intervention and Endarterectomy in Patients with Femoral Artery Disease: A Systematic Review
Bibliographic record
Abstract
Abstract Aim To compare the safety and efficacy of endovascular stenting to endarterectomy in treating femoral artery disease. Method We searched databases such as PubMed, Ovid, Embase and Scopus for studies done on endovascular and endarterectomy treatments in the last decade. The inclusion criteria comprised of latest studies within the last 10 years, human studies and restricting to only femoral artery disease. Relevancy of the articles were assessed using PRISMA and the Newcastle-Ottawa quality assessment scale. Thereafter, the data were extracted, and the statistical variables were calculated using MedCalc software. The PRISMA checklist was used to assess risk of bias. Results Total of twenty-six retrospective, prospective and sub-analysis studies met the inclusion criteria with a total of 7126 patients (endovascular, 2496; endarterectomy, 4630). A division of the outcomes into primary and secondary were assessed based on direct correlation to the parameters evaluated. Our review demonstrated a higher value of technical success for endovascular intervention than endarterectomy with an odds ratio of 4.27 and statistically significant at 95% CI (2.87–6.38). In terms of safety, wound complications, and mortality (short and long-term) were evaluated. For wound infection, endovascular had better odds ratio of 0.65; 95% CI [0.51 to 0.81] and thus preferrable. Delayed wound healing and dehiscence occurred only in 0.15% of endovascular procedures in contrast to 0.99% in endarterectomy. There was no significant difference in both the mortality rates in the two groups appraised. Conclusions Endovascular treatment of femoral artery disease is a safe and effective alternative to endarterectomy. It has a lower incidence of wound related and other perioperative complications.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".