Bibliographic record
Abstract
The objectives of hemodialysis have moved from the diffusive clearance of small molecular weight uremic toxins and achieving dialyzer urea adequacy targets to emphasis on improving clinical outcomes in end stage renal failure patients by increasing larger sized uremic toxin clearance. Clinical emphasis in the last few decades has focused on increasing middle molecule weight toxin clearance by hemodiafiltration. Although long-term data is still lacking, short-term outcomes appear promising. Advancements in nanotechnology have now introduction a new generation of medium cut-off membrane dialyzers which allow diffusive clearance of similar middle molecular weight uremia toxin clearance as hemodiafiltration, without increased albumin losses. As these dialyzers have only recently been introduced into clinical practice, no long-term outcomes are available to determine the relative benefits or advantages of this approach. As dialyzers are now designed to maximize diffusive or convective clearance, or provide a combination, then clinicians can now choose dialyzers tailored to the individual patient needs depending on clinical circumstances. We review the key important features in choosing a dialyzer for patients with end stage renal failure and acute kidney injury.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".