Pilot study to assess increased dialysis efficiency in patients with limited blood flow rates due to vascular access problems
Bibliographic record
Abstract
In the last few years, the number of hemodialysis patients with inadequate blood flow (Qb) rates has increased due to vascular access problems. To avoid a clinical status of underdialysis, these patients need long-lasting dialysis sessions. However, other factors aimed to optimize the dialysis dose have to be considered. High-efficiency convective therapies, such as online hemodiafiltration (HDF), are claimed to be superior to high-flux hemodialysis (HF-HD) in improving the dialysis efficacy, but treatment efficacy is strongly related to blood flow rate and infusion volumes. Online mid-dilution (HDF-MD) with the Nephros OL-pure MD190 represents a new HDF concept to increase the removal of middle molecules. In a cross-over clinical trial, 8 patients, with Qb eff <300 mL/min, received either online HDF-MD or HF-HD; Qd was 700 mL/min, the time duration was 240 min, and the filtration volume in HDF-MD was 112+/-7 mL/min. No differences were found for Kt/V, urea, and creatinine clearances. Clearance of both small phosphate (P) large beta(2)-microglobulin (beta(2)m), and leptin (L) solutes was significantly greater for MD (P 217+/-32, beta(2)m 85.5+/-10, L 42.6+/-18 mL/min) than for HF-HD (P 178+/-32, beta(2)m 71.9+/-13, L 32.1+/-12 mL/min). The results of this study indicate that HDF remains the best means of providing increased removal of large-molecular weight solutes even in patients with vascular access problems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".