The epidemiology of early deep vein thrombosis in kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: Because kidney transplant recipients may be at increased risk for deep vein thrombosis (DVT) following transplantation, we investigated the incidence, risk factors, treatments and outcomes of early DVT among kidney transplant recipients. METHODS: An observational, single-centre cohort study was conducted among adult kidney transplant recipients from Jan. 1, 2005, to Dec. 31, 2016 with 1-year followup. Time to DVT was assessed using the Kaplan-Meier method. Cox proportional hazards and linear regression models were used to analyze risk factors for and outcomes of DVT. RESULTS: The cumulative incidence of DVT was 4.25% at 3 months after transplant. In multivariable analysis, the use of depleting induction agents (hazard ratio [HR] 2.13, 95% confidence interval [CI] 1.05-4.35]), white recipient race (HR 1.84. 95% CI 1.08-3.12), the use of kidneys from expanded criteria donors (HR 2.13, 95% CI 1.05-4.32) and lower recipient body mass index (HR 0.95, 95% CI 0.91-1.00) increased the risk for early DVT. Peritransplant DVT prophylaxis was not associated with early DVT. Early DVT was not associated with reduced graft function, death, graft failure or first hospital readmission. CONCLUSION: Risk factors for early DVT in our cohort of kidney transplant recipients included white recipient race, use of depleting agents, lower recipient body mass index and use of expanded criteria donors. As practice patterns of donor and recipient selection in kidney transplantation evolve, the results of this study may aid in perioperative risk assessments and decision-making about the use of DVT prophylaxis.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".