The role of non-invasive Arterial Stiffness diagnostics in perioperative risk stratification for CABG
Bibliographic record
Abstract
Background: Arterial Stiffness (AS) is a novel marker shown to be an independent predictor of coronary artery disease (CAD). However, AS has been minimally studied for its predictive value in CAD outcomes post-management. This review summarizes the current knowledge of AS and its potential role as a risk stratifying marker in coronary bypass graft (CABG) patients. Methods: A scoping review was performed to discover the existing primary research on AS and its role as a predictive marker of outcomes in CAD. MeSH terms were formulated for the database search. A pre-set inclusion and exclusion criteria were created for appropriate article retrieval. A qualitative analysis and syntheses of these articles was conducted. Results: The search returned a total of 11 articles from 2005 to 2020 discussing the prognostic implications of AS in CAD management. A majority of the articles found prognostic value of AS in medically-managed and percutaneous-revascularized patients. Only 3 studies examined outcomes of CABG in-relation to pre-operative AS, which were extremely limited in scope, though did show prognostic correlation of post-op acute kidney injury. Conclusions: Despite the strong relationship of AS and CAD, few studies have looked at its prognostic value in patients undergoing surgical revascularization. Given the expanding evidence for AS as a marker of CAD and the progression of point-of-care technology to assess AS, this state-of-the-art measure could be a valuable risk stratifying tool for surgeons considering CABG for their patients, though further original research is needed.
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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".