Beyond ultrapure hemodialysis: A necessary and achievable goal
Bibliographic record
Abstract
Abstract Survival of chronic hemodialysis patients is worse than that of many patients with cancers or severe infections. An important cause of chronic inflammation is impurities infused into patients during dialysis. Definitions of dialysis purity have been narrow and focused on metals in dialysate water and on bacterial contaminants. There is no standard for priming fluids or toxins released directly into blood from inside the extracorporeal circuit. We propose a much broader standard of dialysis purity that also includes phthalate metabolites, bisphenols, spalled particles, and other contaminants from dialysis machines, filters, and bloodlines. Standards must include new methods for measuring bacteriological contaminants in addition to colony‐forming units and endotoxin determinations. These include the sensitive silkworm larva plasma test that detects peptidoglycan that is missed by endotoxin tests and standards for newly detected small molecular bacterial detritus. Current levels for “standard” bacteriological contaminants are woefully inadequate and should be increased. New standards for contamination with plasticizers and spallation are also necessary. Studies with ultrapure dialysis have shown almost immediate patient benefits with increased well‐being and stabilization of the cardiovascular system during and between dialyses. Intermediate effects include lower C‐reactive protein levels, better response to erythropoietin, increased appetite, and improved nutrition. Over the years, amyloidosis and carpal tunnel syndrome have become less common and cardiovascular deaths have decreased. Standards for dialysis purity must be sharpened and expanded and this becomes even more urgent with daily and long nightly hemodialysis. All contaminants received by patients, whether biological, chemical, or physical, must be considered.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".