The Effect of Increased Frequency of Hemodialysis on Volume-Related Outcomes: A Secondary Analysis of the Frequent Hemodialysis Network Trials
Bibliographic record
Abstract
In previous reports of the Frequent Hemodialysis Network trials, frequent hemodialysis (HD) reduced extracellular fluid (ECF) and left ventricular mass (LVM), with more pronounced effects observed among patients with low urine volume (UVol). We analyzed the effect of frequent HD on interdialytic weight gain (IDWG) and a time-integrated estimate of ECF load (TIFL). We also explored whether volume and sodium loading contributed to the change in LVM over the study period. Treatment effects on volume parameters were analyzed for modification by UVol and the dialysate-to-serum sodium gradient. Predictors of change in LVM were determined using linear regression. Frequent HD reduced IDWG and TIFL in the Daily Trial. Among patients with UVol <100 ml/day, reduction in TIFL was associated with LVM reduction. This suggests that achievement of better volume control could attenuate changes in LVM associated with mortality and cardiovascular morbidity. TIFL may prove more useful than IDWG alone in guiding HD practice. Video Journal Club 'Cappuccino with Claudio Ronco' at http://www.karger.com/?doi=441966.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".