Whole‐blood viscosity increases significantly in small arteries and capillaries in hemodiafiltration. Does acute hemorheological change trigger cardiovascular risk events in hemodialysis patient?
Bibliographic record
Abstract
Whole-blood viscosity is increasingly being recognized as a factor implicated in the vascular disease progression in high-risk chronic kidney disease patients. Intermittent hemodialysis and hemodiafiltration sessions, characterized by rapid volume changes and anemia correction by erythropoietin stimulating agents, are favorable conditions for enhancing whole-blood viscosity changes and consequently triggering cardiovascular events. To evaluate whole-blood viscosity changes induced by hemodiafiltration, a cross-sectional study has been performed in a group of 28 stable patients. In order to assess the impact of vessel size on whole-blood viscosity changes, we performed a dynamic whole viscosity analysis on a wide spectrum of shear rates reproducing vasculature hemorheologic conditions. Blood viscosity changes are dependent on patient characteristics, hemoglobin, and total plasma protein concentrations. Whole blood viscosity increases significantly during hemodiafiltration over the complete spectrum of shear rates. Dynamic whole-blood viscosity (WBV) increases up to 60%, predominantly at low shear rates in small arterioles and capillary beds. This observation underlines the potential pathogenic contribution of WBV increase in capillaries triggering cardiovascular events in the postdialysis period. Eight patients died from cardiovascular events. Higher WBV increase was noted in this group but did not reach statistical significance due to the insufficient power of the study. Hemorheological changes associated with WBV increase in capillary beds may contribute to aggravate silent tissue hypoxemia and precipitate cardiovascular events in chronic kidney disease patients. Prospective studies specifically designed and powered to evaluate the impact of WBV changes on cardiovascular events in dialysis patients are required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".