The effect of high‐flux hemodialysis and post‐dilution hemodiafiltration on platelet closure time in patients with end stage renal disease
Bibliographic record
Abstract
Abstract Introduction: We aimed to compare prospectively the effect of high‐flux hemodialysis and post‐dilution hemodiafiltration on platelets. Methods: Twenty‐two hemodialysis patients were treated with one high‐flux hemodialysis and one post‐dilution hemodiafiltration procedure. PFA‐100 closure times (collagen/epinephrine—CEPI and collagen/adenosine diphosphate—CADP) were measured before and after the procedure, as well as platelet count, hemoglobin, hematocrit, and red blood cell count. All pre‐dialysis and post‐dialysis samples were taken from the afferent line. Findings: The platelet count after vs. before hemodialysis did not change significantly (229.3 ± 55.0 x109/L vs. 233.6 ± 55.8 × 109/L; P = 0.269), but was significantly lower after post‐dilution hemodiafiltration (215.5 ± 51.7 × 109/L vs. 245.3 ± 59.9 × 109/L; P < 0.0001). CEPI after vs. before hemodialysis was not significantly prolonged (192.9 ± 60.8 s vs. 173.4 ± 52.5 s; P = 0.147), and the same applied to CADP (143.6 ± 40.3 s vs. 142.6 ± 38.4 s; P = 0.897). CEPI after vs. before post‐dilution hemodiafiltration was significantly prolonged (268.3 ± 41.3 s vs. 176.4 ± 54.0 s; P < 0.0001) as was CADP (221.0 ± 53.9 s vs.133.9 ± 31.1 s; P < 0.0001). Discussion: Only after post‐dilution hemodiafiltration, we found a lower platelet count and prolonged platelet closure times.
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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) | 0.000 | 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 | 0.001 |
| 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".