Reliability of delivered dialysate sodium concentration
Bibliographic record
Abstract
Abstract Background The results of studies investigating the effects of hyponatraemic dialysates have been mixed, with some reporting positive effects including reduction in blood pressure and inter‐dialytic weight gains, whereas others have not been able to demonstrate any effect. These studies assume that setting a lower dialysate sodium results in the delivery of a hyponatraemic dialysate. We therefore measured delivered sodium to determine reliability. Methods We measured dialysate sodium in 10 BBraun Dialog+® and 6 Fresenius 4008H dialysis machines, which had been set up to deliver a sodium of 136 mmol/L, using flame photometry and indirect ion selective electrode (ISE) methods. Results Dialysate conductivity was 13.85 ± 0.05 mS/cm, but dialysate sodium measured by flame photometry was 141.8 ± 2.9 mmol/L, and 142.5 ± 2.4 mmol/L by ISE. Both dialysis machines delivered a dialysate sodium in excess of the 136 mmol/L set, with a mean bias of 7.0 ±2.1 mmol/L for the Dialog+®, and 3.7 ± 2.6 for the 4008 with the flame photometer method, and a mean bias of 6.3 ± 1.3 mmol/L for the Dialog+®, and 6.8 ± 3.7 for the 4008 by ISE. Conclusion It is assumed when setting a dialysate sodium concentration that this sodium concentration is delivered. However we found that the dialysate sodium concentration delivered was greater than that set, despite the dialysis machines reporting a conductivity measurement in keeping with a lower sodium dialysate. Trials of lowered dialysate sodium therefore need to measure dialysate sodium concentrations to ensure what has been set is delivered.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".