Individualized dialysate sodium prescriptions using sodium gradients for high‐risk hemodialysis patients lowered interdialytic weight gain and achieved target weights
Bibliographic record
Abstract
INTRODUCTION: Large interdialytic weight gain (IDWG) is associated with increased morbidity and mortality in chronic hemodialysis patients. Over 50% of patients at our inner city tertiary academic center dialysis unit had IDWG and target weights (TW) above goal. We conducted an open-label nonrandomized study to explore the effects of an individualized dialysate sodium (DNa) prescription using Na gradients in patients at high risk for large IDWG. Thirty-three patients receiving chronic hemodialysis received individualized DNa prescriptions with a DNa bath of 0 to -2 meq/L below their serum Na level in the intervention group, while patients in the control group were prescribed the standard dialysate Na at 138 mmol/L. Serum Na level, predialysis SBP, symptomatic hypotensive episodes, and %hemodialysis treatments with large IDWG (%TxAIDWG) and above TW(%TxATW) were recorded before and three months after the intervention. We used student t tests to compare continuous variables and Chi-square tests to compare binary variables between the groups at baseline and after the intervention. Age- and sex-adjusted linear regression models were also constructed to assess the differences in each continuous outcome between the groups. Multivariable logistic regression models were conducted by modeling IDWG decrease and above estimated-dry-weight (EDW) decrease as binary dependent variables with adjustment for age, sex, and EDW change. FINDINGS: Patients with individualized DNa concentrations had 3.6 times greater odds of having lower IDWG than those with standard dialysate Na concentration. This significant association remained after adjustment for age, sex, and changes in EDW (OR: 3.63; 95% CI, 1.03-12.9). There was no difference in predialysis BP or symptomatic hypotensive episodes between the two groups. DISCUSSION: Individualized DNa prescriptions appeared to be well tolerated and may be effective for optimal fluid management in high-risk hemodialysis 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 | 0.001 |
| 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".