Overview of clinical studies in hemodiafiltration: What do we need now ?
Bibliographic record
Abstract
Despite several technical advances in dialysis treatment modalities and a better patient care management including correction of anemia, suppression of secondary hyperparathyroidism, lipid and oxidative stress profiles improvement, the morbidity and the mortality of dialysis patients still remain still elevated. Recent prospective interventional trials in hemodialysis (HEMO study and 4D study) were not very conclusive in showing any significant improvement in dialysis patient outcomes. High-efficiency convective therapies, such as online hemodiafiltration (HDF), are claimed to be superior to conventional diffusive hemodialysis (HD) in improving the dialysis efficacy and in reducing intradialytic morbidity and all-cause and cardiovascular mortality in dialysis patients. The aim of this report was, first, to review the evidence-based facts tending to prove the superiority of HDF vs. HD in terms of efficacy and tolerance, and, second, to analyze the needs to prove the clinical superiority of HDF in terms of reducing morbidity and all-cause mortality of dialysis patients. A systematic review of studies comparing HDF and HD has been performed in the microbiological safety of online production, the solute removal capacity of small and medium-size uremic toxins, and its implication in the reduction of the bioactive dialysis system vs. patient interaction. Major planned randomized international studies comparing HDF and HD in terms of morbidity and mortality have been reviewed. To conclude, it is thought that these long-term prospective randomized trials will clarify on a scientific evidence-based level the putative beneficial role of high-efficiency HDF modalities on dialysis patient outcomes.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".