The individually optimized bolus dose of nadroparin is safe and effective in diabetic and nondiabetic patients with bleeding risk on hemodialysis
Bibliographic record
Abstract
The risk of bleeding is a well-known complication in patients on hemodialysis (HD). The aim of this prospective study was to determine the lowest single bolus dose of low-molecular-weight heparin nadroparin for safe and effective HD in patients with a bleeding risk. Forty HD patients were divided into 4 subgroups with 10 participants (diabetics with and without a bleeding risk, nondiabetics with and without a bleeding risk). The actual starting bolus dose was decreased by 25% after the initial 4 weeks, further decreased by 25% of the starting dose after 4 weeks, and changed due to extracorporeal circuit clotting in the last 4 weeks. The parameters of coagulation were measured at the beginning, after 2 and 4 h of HD sessions. A significant reduction of nadroparin (first vs. last HD session) was observed in: diabetics with a bleeding risk (49.66 ± 12.33 vs. 28.78 ± 9.60 IU/kg/HD; P<0.001), diabetics without a bleeding risk (50.70 ± 15.23 vs. 33.95 ± 16.97 IU/kg/HD; P<0.001), and nondiabetics with a bleeding risk (61.25 ± 18.68 vs. 32.96 ± 10.06 IU/kg/HD; P<0.001). Altogether, the reduction of the nadroparin dose in these groups was 42.05%; 33.04%, and 46.19%, respectively. Although anti-Xa at hour 4 at the end of the study was <0.4 IU/mL in our diabetic and nondiabetic patients without a risk of bleeding, serious clottings in the extracorporeal circuit and vascular access thromboses were not found. This study demonstrated for the first time that individually optimized doses of nadroparin are sufficient for safe and effective HD in patients with a bleeding risk.
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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 | 0.001 |
| 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) | 0.000 | 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".