Platelet activation and clotting cascade activation by dialyzers designed for high volume online hemodiafiltration
Bibliographic record
Abstract
INTRODUCTION: Hemodialysis patients are pro-thrombotic. Higher volume online postdilutional hemodiafiltration (OL-HDF), with increasing hematocrit increases the risk of clotting in the extracorporeal circuit (ECC). We wished to determine whether OL-HDF increased platelet activation and ECC clotting. METHODS: Coagulation parameters, platelet, white cell, and endothelial activation markers were measured at the start and end of dialysis sessions in 10 patients and also pre- and post-dialyzer after 15 minutes using two different dialyzers designed for high volume OL-HDF; cellulose triacetate (TAGP) and polysulphone (PS), and polyvinylpyrrolidone (PVP). Patients were anticoagulated with a heparin bolus. FINDINGS: At the start of OL-HDF, D dimers, thrombin antithrombin complexes (TATs), and soluble adhesions molecules (sICAM-1 and sVCAM-1) were increased. Post-treatment soluble P selectin (PS/PVP 26.7 ± 7.1 versus 36.6 ± 9.9; TAGP 28.7 ± 7.2 versus 43.5 ± 8.4 ng/ml, P < 0.001), and soluble CD40 ligand (PS/PVP 297 ± 228 versus 552 ± 272, TAGP 245 ± 187 versus 390 ± 205 ng/ml, P < 0.05) increased. Post-dialyzer concentrations increased versus pre-dialyzer for tissue factor (PS/PVP 117 ± 12 versus 136 ± 16, TAGP 100 ± 25 versus 128 ± 40 ng/ml, P < 0.05), factor VIIIc (PS/PVP 174 ± 54 versus 237 ± 83, TAGP 163 ± 60 versus 247 ± 102 IU/ml, P < 0.01), sVCAM-1 (PS/PVP 782 ± 64 versus 918 ± 140, TAGP 722 ± 121 versus 889 ± 168 ng/ml, P < 0.01), and D-dimers (PS/PVP 292 ± 132 versus 355 ± 167, TAGP 300 ± 129 versus 391 ± 171 ng/ml, P < 0.001). There was no macroscopic thrombus noted in the ECC, and no increase in microparticles, platelet factor-4, or TATs. DISCUSSION: Despite being pro-thrombotic, with activation of platelets, and lymphocytes during passage through ECC, no macroscopic clotting, or increased TATs were noted during OL-HDF, and no major differences between cellulosic and polysulphone dialyzers.
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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 | 0.000 | 0.000 |
| 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".