Online measurement of hemodialysis adequacy using effective ionic dialysance of sodium—a review of its principles, applications, benefits, and risks
Bibliographic record
Abstract
Abstract Dialysis dose is an important determinant of clinical outcomes in patients with end stage renal disease on maintenance dialysis. In clinical practice dialysis dose is monitored at least monthly by urea clearance based on Urea Kinetic Modeling. Online clearance monitoring using effective ionic dialysance (EID) of sodium (Na+) is available on some hemodialysis machines. This paper reviews the background, methodology, additional applications, and potential risks associated with EID. Effective ionic dialysance provides a reliable, real‐time, noninvasive, and inexpensive measurement of dialysis dose during an ongoing hemodialysis (HD) session to allow interventions and assess the impact of these changes on clearance. Surveillance of vascular access flow rates can be used to screen for access dysfunction and refer for interventions. There is a concern that EID measurements may cause Na+ loading because of high dialysate Na+ used during these measurements, however, mathematical models, in vitro experiments, and clinical studies in patients on maintenance HD do not show any evidence of Na+ loading during EID measurements. We cannot rule out the possibility of nonosmotic Na+ accumulation in the skin because no published literature exists on this topic as it pertains to clearance measurements based on EID of Na+.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".