Which solute removal mechanism dominates over others in dialyzers for continuous renal replacement therapy?
Bibliographic record
Abstract
Selecting dialyzers is more important for blood purification in critical care because other therapeutic options (especially amount of dialysate) are usually limited. Adsorption is a major mechanism of removing toxic substances from patients in these treatments. Several commercial dialyzers designed for acute blood purification therapy were investigated by performing aqueous ultrafiltration experiments to demonstrate the adsorptive importance of membrane materials. Polymethylmethacrylate showed strong adsorptive characteristics to α-chymotripsinogen A (MW 25,000) but showed much less adsorptive characteristics to cytochrome C (MW 12,400). On the contrary, polyacrylonitrile showed relatively lower affinity and polysulfone showed essentially no affinity to these proteins. Time-dependent and concentration-dependent characteristics of clearance for these solutes were also demonstrated in polymethylmethacrylate. Adsorption found in polymethylmethacrylate may be due to the occlusion of protein molecules into pores of the membrane. Choosing membrane materials is, therefore, important not only in removing inflammatory cytokines but also for accounting for the loss of albumin in acute and chronic treatments.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".