Dose Effect Relationship of Reviparin in Chronic Hemodialysis: A Crossover Study Versus Nadroparin
Bibliographic record
Abstract
Low molecular weight heparins (LMWHs) are used for prevention of clotting in the dialysis circuit. The aim of this trial was to define the optimal dose of a new LMWH and to test the efficiency of a single dose at the start of the session. Fifteen patients were treated according to a double blind and crossover design during 4 blocks of 5 consecutive reviparin doses assigned randomly as 50, 60, 70, 85, and 100 IU anti-Xa/kg. Assessment was carried out on screening of fibrin rings or clots in the arterial and venous air traps and on visual detection of fiber in the dialyzer after rinsing. These clinical results were compared to plasmatic anti-Xa activity and thrombin-antithrombin (TAT) complex generation. A standard dose of 70 IU anti-Xa/kg of nadroparin was used as the control. After a bolus of 50 to 100 IU anti-Xa/kg, the occurrence of fibrin rings and clots in the air traps was dependent on three factors: dose of LMWH, time of the session, and patient status. A bolus of 85 IU anti-Xa/kg of reviparin was effective and safe for sessions of 4 h. For this dose, plasmatic anti-Xa activity was 0.96 +/- 0.28 IU/ml at Hour 2 and 0.82 +/- 0.22 IU/ml at Hour 4. TAT complexes are good markers of the activation of the coagulation. They did not increase during a 4 h session after a reviparin bolus of 100 IU/kg. For the same LMWH dose, the trial shows a great variability of the clinical effect and anti-Xa activities from one patient to another. A single dose of 85 IU anti-Xa/kg of reviparin can be used at the start of the dialysis session as a loading dose. We advise adapting the dose during the subsequent sessions according to the appearance of the blood circuit. The benefit of monitoring anti-Xa activity and TAT complexes could be tested in a further trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".