The effect of hemodialysis ultrafiltration on changes in whole blood viscosity
Bibliographic record
Abstract
Increased whole blood viscosity (WBV) can be injurious to the vascular endothelium and increase the risk of atherothrombotic events. This study examined the effect of hemodialysis ultrafiltration (UF) on WBV, with a focus on high vs. low-volume UF patients. In stable hemodialysis patients, blood was drawn for hematocrit (Hct) and WBV at the start, midpoint, and at the end of dialysis. For analysis, patients were divided into high UF (≥2700 mL) or low UF (<2700 mL) groups. A total of 59 patients completed the study. Mean Hct increased during dialysis in both groups. The intradialytic increase in Hct was significantly greater in the high vs. the low UF group (3.2% vs. 1.28%, P = 0.01), with a significantly higher end-dialysis Hct in the high UF group (40.5% vs. 38%, P = 0.02). At the end of dialysis, both high shear rate WBV (P < 0.01) and low shear rate WBV (P < 0.01) were significantly higher in the high UF compared with the low UF group. There was an approximately two-fold greater increase in high shear rate (P < 0.01) and low shear rate (P = 0.01) WBV during dialysis in high vs. low UF groups. The increase in high shear rate WBV during dialysis was significantly correlated with an increase in Hct (R(2) = 0.63, P < 0.01). We found that hemodialysis UF causes a surge in WBV. The surge was of greater magnitude in high than in low UF patients.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".