A narrative review of the role of endoscopically assisted in situ bypass in the modern era of limb salvage vascular bypass
Bibliographic record
Abstract
Objective In this narrative review, we seek to summarize key literature describing non-traditional ‘minimally invasive' in-situ lower extremity bypass techniques. We described the various historical as well as newer attempts, and their known outcomes, insofar. We particularly focused on the sparsely-used endoscopic assisted in-situ bypass technique. Methods A list of search terms and keywords relevant to novel in-situ bypass techniques were identified. A retrospective review of the literature was conducted screening PubMed/MEDLINE and Scopus with search period from January 1, 1959 to August 1, 2023. Results Our search yielded six previous studies that have utilized various permutations of the novel in-situ bypass techniques. Despite the relative paucity of high-quality data, the studies demonstrate that endoscopic technique has lower wound complications rates, shorter hospital stays, and no significant differences in outcomes compared to traditional in-situ bypass technique. Conclusions The endoscopic in-situ bypass technique demonstrates important wound related benefits compared to the traditional in-situ technique. This minimally invasive approach is certainly in keeping with the current technical knowledge and skillset in vascular surgery. Future studies are needed to systematically compare long term outcomes.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".