Moving beyond small solute clearance: What evidence is there for more permeable dialyzers and haemodiafiltration?
Bibliographic record
Abstract
Dialyzers were initially developed for diffusive clearance of uraemic toxins. Diffusion most effectively clears small uncharged solutes from plasma water, such as urea. Sessional urea clearance targets have been shown to be important for short-term patient survival, but over the longer term, although low-flux dialysis can prolong patient survival, accumulation of middle-sized uraemic toxins, such as β2 microglobulin can lead to disabling arthropathy. Although the introduction of high-flux dialyzers, designed to increase β2 microglobulin clearance, has reduced the prevalence of arthropathy; this has not been translated into a demonstrable significant improvement in patient survival. However, analysis of individual patients recruited into trials of haemo-diafiltration reported that greater convective clearance was associated with better survival, although the individual trials reported mixed outcomes. Most haemodiafiltration trials were not designed to study the effect of convective dose, so although reported patient survival was greater for those receiving greater convective volume exchange, these results could potentially be confounded by patient or center effects. An alternative approach to increasing middle-sized solute clearances would be to use more permeable dialyzers, but as yet there are no trials reporting survival with larger cutoff dialyzers. As such, although there is increasing evidence that increasing middle-sized molecular uraemic solute clearance is associated with improved patient survival, further prospective trials are required to determine whether as with Kt/Vurea there is a threshold effect of how much convective or middle-sized solute clearance is required to improve patient survival.
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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 | 0.031 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".