Dialysis Efficacy at Rest and During Intra‐dialytic Exercise: Agreement between Serum and On‐line Kt/V
Bibliographic record
Abstract
The agreement between serum Kt/V values and on‐line Kt/V values was examined at rest and during intra‐dialytic exercise (IDE). On the middle dialysis session of the week, participants (n=10) underwent dialysis as usual for 2 weeks and cycled for 45–60 min during dialysis for another 2 weeks. Serum urea concentrations were measured pre‐ and post‐dialysis for calculation of Kt/V using the Jindal (JKt/V) and the Daugirdas (DKt/V) second generation equations. The On‐line Kt/V (OKt/V) value was recorded at the end of dialysis (Fresenius 2008K). All dialysate effluent was collected and the total urea (mmol) eliminated during each session was determined. No significant differences in Kt/V and total urea clearance values were found between rest and IDE. Excellent correlations were found between the JKt/V and DKt/V values at rest and during IDE (r=0.97). Very good associations occurred between OKt/V and the JKt/V and DKt/V values at rest and during IDE (r=0.83–0.86). Bland Altman analysis indicated a significant negative bias between the JKt/V value with respect to both the DKt/V (−0.128) and OKt/V (−0.163) values at rest and during IDE (−0.097, −0.203); as well as between the DKt/V and OKt/V values (−0.106) during IDE. The On‐line Kt/V provides the most conservative estimation of dialysis efficacy and potentially may be the best referral value from which to ensure achievement of minimally acceptable urea clearance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".