Impact of inflammation on anti‐oxidative effects of vitamin <scp>E</scp>‐coated membrane dialyzer in patients on chronic hemodialysis
Bibliographic record
Abstract
Hemodialysis (HD) with the use of vitamin E-coated membrane (VEM) dialyzers is shown to exert anti-inflammatory and antioxidative effects in patients with end-stage renal disease on HD. However, the association of baseline inflammatory status with the antioxidative effects of VEM has not been investigated thus far. Thirty-five stable end-stage renal disease patients treated with VEM for 6 months were enrolled in the present prospective, observational cohort study. For the previous 3 months minimum, 17 (48%) patients were dialyzed with a cellulose, eight (23%) patients with a hemophane, and 10 (29%) patients with a polysulfone 1.2 to 1.5 m(2) hollow fiber dialyzer. The effects of treatment on oxidized low-density lipoprotein (oxLDL) were stratified according to half percentiles of baseline serum logC-reactive protein and interleukin-6, and the association between treatment goal, arbitrarily defined as a minimum 30% decrease in baseline oxLDL, was assessed with the use of logistic regression analysis. The higher C-reactive protein and interleukin-6 half percentiles were independently and additively associated with a higher odds ratio for achieving treatment goal. Adjustment for baseline oxLDL, age, sex, HD duration, smoking, and body mass index did not attenuate the odds ratios, whereas the history of diabetes, as primary renal disease, significantly decreased the odds ratio for achieving treatment goal. Increased baseline C-reactive protein and interleukin-6 are independent, additive factors associated with the effect of VEM on oxLDL in HD patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".