What is a surrogate marker for optimal dialysis?
Bibliographic record
Abstract
To find a surrogate marker to obtain optimal dialysis delivery from the viewpoint of nutrition, 180 maintenance hemodialysis patients (109 males/71 females) were enrolled between October 1999 and June 2006 at our kidney center. In the 449 hemodialysis treatments, ultrapure dialysis solutions and high-flux synthetic membranes were utilized. Parameters were measured by Kt/V(urea) and postdialysis urea rebound, Kc (the cellular membrane clearance for urea), urea clear space (CS), %creatinine generation rate, %lean body mass, total body water, and so on. We examined the correlation between dialysis delivery and nutritional parameters: Kt/V(urea) and postdialysis urea rebound were found to be strongly and negatively correlated with nutritional parameters. However, Kc and CS have shown positive and strong correlations with nutritional parameters such as %creatinine generation rate, %lean body mass, and total body water as well. In addition, the age factor was correlated with Kt/V(urea) positively, and it influenced Kc and CS negatively. As a conventional dialysis parameter, Kt/V(urea) did not reflect nutrition, but Kc was found to improve nutrition due to the increase of the dialysis delivery. Therefore, Kc might be a reliable surrogate marker for optimal dialysis.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".