Bibliographic record
Abstract
Abstract Ultrapure dialysis fluid contains less than 0.1 CFU/mL and 0.03 EU/mL and can be prepared by ultrafiltration of standard‐quality dialysis fluid. Today, the use of ultrapure dialysis fluid is widely recommended based on our awareness of potential transfer of bacterial products across dialysis membranes. Early observations associated improved fluid quality with reduced incidence of the carpal tunnel syndrome, but provided no proof of mechanism. Recent clinical studies in hemodialysis patients have shown that the introduction of ultrapure dialysis fluid brings about significant improvements in a number of inflammation‐related parameters. Levels of C‐reactive protein and IL‐6 are reduced, anemia management is achieved using less EPO or reaching higher hemoglobin levels, nutritional indices are improved, and β2‐microglobulin levels have been observed to decline, when ultrapure dialysis fluid is used in comparison with standard‐quality fluid. In addition, some studies have documented reduced levels of advanced glycation end products and delayed decline of residual renal function in patients using ultrapure dialysis fluid. Although further evidence is needed before we can assess the long‐term benefits associated with improved fluid quality, there should be sufficient data available today to support our efforts toward gradual improvement of the microbiological quality of dialysis fluid.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".