Assessment of prophylactic heparin infusion as a safe preventative measure for thrombotic complications in pediatric kidney transplant recipients weighing <20 kg
Bibliographic record
Abstract
Small-sized kidney recipients (<20 kg) are at high risk of allograft vessel thrombosis. HP has been used to mitigate this risk but may infer an increase in bleeding risks. Therefore, we aim to determine whether HP is a safe means to prevent thrombosis in small kidney transplant patients by comparing those who have received HP and those who have NHP. A retrospective review of patients < 20 kg who underwent kidney transplant in our institution from 2000 to 2015 was performed. At our institution, unfractionated heparin 10 units/kg/hour is used as HP since 2009. Patients at increased risk of thrombosis (previous thrombosis, thrombophilia, nephrotic syndrome) and bleeding (therapeutic doses of heparin, diagnosis of coagulopathy) were excluded. Fifty-six patients were identified (HP n = 46; NHP n = 10). Baseline demographics were similar between HP and NHP. There was no statistical difference in frequency of transfusions, surgical re-exploration, or thrombotic events between HP and NHP. The HP group was more likely to have drop in Hb > 20 g/L (67.4% vs 30.0%, P = 0.038), and those who had drop in Hb > 20 g/L were more likely to also require pRBC transfusions (63.0% vs 20.0%, P = 0.017). Within the HP group, those who had bleeding complications had similar Hb levels as those who did not at baseline and post-transplant. Outcomes in the HP and NHP groups were no different with respect to thrombosis or significant bleeding complications requiring pRBC transfusions or surgical intervention. Future prospective studies are required to investigate the balance of preventing thrombosis and risks of pRBC transfusions for small-sized kidney recipients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".