Assessing the contact‐activation of coagulation during hemodialysis with three different polysulfone filters: A prospective randomized cross‐over trial
Bibliographic record
Abstract
INTRODUCTION: During hemodialysis (HD) the interaction of the blood with the dialyzer triggers both an inflammatory reaction and an activation of the coagulation cascade. An accepted parameter to quantify the extent of coagulation activation during HD is not available. This study aims to evaluate its amplitude, comparing dialyzers made of different polysulfone polymers, by measuring D-dimers in the filter-rinsing fluids (Frf) and to test whether Frf D-dimers are suitable candidate markers to assess contact coagulation activation during HD. METHODS: In a prospective, cross-over study 41 hemodialysis patients were randomly allocated to nine HD sessions with three types of polysulfone membranes: Filter A: Poliflux®RevaclearMAX; Filter B: Helixone®Fx80, Filter C: Polyflux®H210. FINDINGS: A total of 117 HD sessions were studied. The mean (SD) filter (Frf) D-dimers were 0.19 µg/L (0.56) for Filter A; 0.66 µg/L (2.81) for Filter B; 0.33 µg/L (1.13) for Filter C. Significant differences were found: A vs. B (P < 0.01), A vs. C (P = 0.01); B vs. C not significant. A large between-patient variability of D-dimer filter levels was found. D-Dimers in blood showed a similar trend but differences were not significant. DISCUSSION: The contact activation of coagulation during HD may also vary among filters made up with similar polysulfones. D-dimer in the filter rinsing fluid but not in the blood can be considered a candidate marker for the evaluation of thrombogenicity during HD. Further studies are needed to elucidate the mechanism(s) and to confirm the usefulness of filter rinsing fluid D-Dimers as a clotting activation marker during HD.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".