Impact of dialysate sodium concentration on vascular refilling
Bibliographic record
Abstract
Abstract Background Although relationship between dialysate sodium concentration and hemodynamic stability has been well studied over the years, outcomes of absolute blood volume (ABV) maintenance and vascular refilling volume (Vref) modifications were not included, as its analysis has not been easily accessible to direct investigation. However, recent studies report a simple and feasible methodology to assess ABV and Vref during hemodialysis (HD) treatments. It is the aim of this study to analyze whether sodium concentration in dialysate modifies ABV drop and Vref. Methods The study was performed in 19 patients under HD. During three different sessions, sodium concentration in dialysate was randomized to three different profiles: low sodium concentration (LNa, 138 mEq/L), neutral sodium concentration (NNa, 140 mEq/L), and high sodium concentration (HNa, 143 mEq/L). ABV and Vref were calculated using Kron et al methodology. Results Predialysis values of the measured parameters showed similar results for the three profiles. Sodium concentration showed an effect on ABV drop, Vref, and vascular refilling fraction (Fref). Pair‐wise comparison revealed mean ABV decreased 0.21 L less when using HNa profile versus LNa profile (p = 0.027), a mean Vref increase of 0.39 L (p = 0.038), and a mean Fref increase of 9.94% (p = 0.048). Conclusions This study shows that the use of HNa profiles increases Vref and Fref and reduces ABV drop during dialysis treatments when compared to LNa profiles.
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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 | 0.001 |
| 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) | 0.001 | 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".