Role of Residual Kidney Function and Convective Volume on Change in β2-Microglobulin Levels in Hemodiafiltration Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Removal of beta2-microglobulin (beta2M) can be increased by adding convective transport to hemodialysis (HD). The aim of this study was to investigate the change in beta2M levels after 6-mo treatment with hemodiafiltration (HDF) and to evaluate the role of residual kidney function (RKF) and the amount of convective volume with this change. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Predialysis serum beta2M levels were evaluated in 230 patients with and 176 patients without RKF from the CONvective TRAnsport STudy (CONTRAST) at baseline and 6 mo after randomization for online HDF or low-flux HD. In HDF patients, potential determinants of change in beta2M were analyzed using multivariable linear regression models. RESULTS: Mean serum beta2M levels decreased from 29.5 +/- 0.8 (+/-SEM) at baseline to 24.3 +/- 0.6 mg/L after 6 mo in HDF patients and increased from 31.9 +/- 0.9 to 34.4 +/- 1.0 mg/L in HD patients, with the difference of change between treatment groups being statistically significant (regression coefficient -7.7 mg/L, 95% confidence interval -9.5 to -5.6, P < 0.001). This difference was more pronounced in patients without RKF as compared with patients with RKF. In HDF patients, beta2M levels remained unchanged in patients with GFR >4.2 ml/min/1.73 m2. The beta2M decrease was not related to convective volume. CONCLUSIONS: This study demonstrated effective lowering of beta2M levels by HDF, especially in patients without RKF. The role of the amount of convective volume on beta2M decrease appears limited, possibly because of resistance to beta2M transfer between body compartments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".