Dialysis desiderata<sup>*</sup>
Bibliographic record
Abstract
Abstract Microarray immunoassay systems, proteomics, separation of polypeptides in plasma by electrophoresis, and detection by mass spectrometry have shown that low molecular weight proteins, cytokines, and chemokines are present in high concentration in chronic kidney disease (CKD) subjects receiving dialysis. That these substances are also found in high concentration in sepsis, postcardiac bypass, acute respiratory distress, burns, and in brain death suggest that these syndromes have a common pathogenesis via a systemic inflammatory response syndrome (SIRS). It is not yet clear whether the profiles of such substances differ among the SIRS states. Dialysis membranes are not capable of removing these substances because such moieties exceed the molecular weight cutoff of modern synthetic hemodialysis membranes. On the other hand, sorbents directly in contact with blood or indirectly with filtered plasma, may be designed with pore structures with the capability of removing these substances. Such sorbents could be exploited in the treatment of CKD. While few human studies have been performed, animal experiments suggest that human trials should be initiated. The potential advantages of sorbents for CKD will be described.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.033 |
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".