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Record W4400618565 · doi:10.1016/j.avsg.2024.06.012

Neuromonitoring during Endovascular Thoracoabdominal Aortic Aneurysm Repair: A Systematic Review

2024· review· en· W4400618565 on OpenAlexaboutno aff
Myat Soe Thet, Mario D’Oria, Davorin Sef, Tomislav Klokočovnik, Aung Oo, Sandro Lepidi

Bibliographic record

VenueAnnals of Vascular Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAortic repairEndovascular aneurysm repairAortic aneurysmSurgeryThoracic aortic aneurysmComplicationAortic surgeryParaplegiaSpinal cord injuryAneurysmEndovascular treatmentAbdominal aortic aneurysmSpinal cordAorta

Abstract

fetched live from OpenAlex

BACKGROUND: Spinal cord ischemia (SCI) is a potentially devastating complication of thoracic endovascular aortic repair (TEVAR) and fenestrated-branched endovascular aortic repair (F-BEVAR). The aim of this systematic review was to evaluate the efficacy of neuromonitoring modalities to mitigate the risk of SCI during TEVAR and F-BEVAR procedures. METHODS: Following the PRISMA guidelines, we conducted a detailed literature search of databases including PubMed, MEDLINE via Ovid, Embase, Scopus, and Cochrane CENTRAL, from 1998 to the present. Inclusion criteria were original research articles examining neuromonitoring during TEVAR and F-BEVAR. The primary outcome was the incidence of SCI, while the secondary outcome included early mortality. The quality of studies was assessed using the Newcastle-Ottawa Scale. RESULTS: From 1,450 identified articles, 11 met inclusion criteria, encompassing data from 1,069 patients. Neuromonitoring modalities included motor-evoked potentials (MEPs), somatosensory evoked potentials (SSEPs), and near-infrared spectroscopy. The combination of MEPs and SSEPs was most commonly used, with 93% sensitivity and 96% specificity for detecting SCI risks. SCI incidence ranged from 3.8 to 17.3%, with permanent deficits occurring in 2.7-5.8% of cases. In-hospital mortality ranged from 0.4 to 8%. Risk factors for SCI were identified, including operation duration and extent of aortic coverage. CONCLUSIONS: Neuromonitoring with MEPs and SSEPs appears to be effective in detecting perioperative SCI risk during TEVAR and F-BEVAR. However, discrepancies between neuromonitoring changes and actual SCI outcomes suggest the need for cautious interpretation. While the incidence of SCI remains variable, identified risk factors may guide clinical decisions, particularly in high-risk procedures. Future research should focus on prospective studies and randomized controlled trials to validate these findings and improve SCI prevention strategies in TEVAR and F-BEVAR.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.128
GPT teacher head0.379
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2024
Admission routes1
Has abstractyes

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