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Record W4393999425 · doi:10.1016/j.jvsvi.2024.100078

A narrative review of the role of endoscopically assisted in situ bypass in the modern era of limb salvage vascular bypass

2024· review· en· W4393999425 on OpenAlexaff
Mufaddal I. Baghdadwala, Alison Michels, Peter Brown, David Zelt, Michael Yacob

Bibliographic record

VenueJVS-Vascular Insights · 2024
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsMcMaster UniversityKingston General HospitalQueen's University
Fundersnot available
KeywordsNarrativeMedicineIn situBypass surgerySurgeryArteryLiteratureChemistryArt

Abstract

fetched live from OpenAlex

ObjectiveIn 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.MethodsA 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.ResultsOur 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.ConclusionsThe 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.305
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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