Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite advances in hemodialysis technology, a stable and well functioning vascular access remains the bane of every hemodialysis patient. It is recognized that vascular access contributes to cardiovascular disease mortality through a number of mechanisms. This review describes the relationship between vascular access and cardiovascular disease by reviewing the relationships between infection risk, inflammation and cardiovascular disease, and the cardiovascular changes that occur as a consequence of vascular access. Improved understanding of these mechanisms and their interrelationship is warranted. RECENT FINDINGS: The impact of arteriovenous fistula creation on cardiac structural and hemodynamic changes is described, as is vascular remodelling, which occurs in response to alterations in blood-flow properties. The development of central and peripheral vein stenosis is also a type of vascular remodelling and consequences of such events are not yet well understood. In addition, the contribution of vascular access to increased inflammation and atherosclerotic disease is reviewed. Finally, the hypothesis that vascular access dysfunction may be a predictor of vascular disease is explored. SUMMARY: The relationship between vascular access and cardiac disease exists at different levels, ranging from inflammation promoting atherosclerotic disease to vascular remodelling changes of stenosis formation and left ventricular hypertrophy. Countless research opportunities abound.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".