Supplementary Material for: Venous Thromboembolism and the Risk of Death and Graft Loss in Kidney Transplant Recipients
Bibliographic record
Abstract
Background: The implications of venous thromboembolism (VTE) for morbidity and mortality in kidney transplant recipients are not well described. Methods: We conducted a retrospective study using linked healthcare databases in Ontario, Canada to determine the risk and complications of VTE in kidney transplant recipients from 2003 to 2013. We compared the incidence rate of VTE in recipients (n = 4,343) and a matched (1:4) sample of the general population (n = 17,372). For recipients with evidence of a VTE posttransplant, we compared adverse clinical outcomes (death, graft loss) to matched (1:2) recipients without evidence of a VTE posttransplant. Results: During a median follow-up of 5.2 years, 388 (8.9%) recipients developed a VTE compared to 254 (1.5%) in the matched general population (16.3 vs. 2.4 events per 1,000 person-years; hazard ratio [HR] 7.1, 95% CI 6.0-8.4; p < 0.0001). Recipients who experienced a posttransplant VTE had a higher risk of death (28.5 vs. 11.2%; HR 4.1, 95% CI 2.9-5.8; p < 0.0001) and death-censored graft loss (13.1 vs. 7.5%; HR 2.3, 95% CI 1.4-3.6; p = 0.0006) compared to matched recipients who did not experience a posttransplant VTE. Conclusions: Kidney transplant recipients have a sevenfold higher risk of VTE compared to the general population with VTE conferring an increased risk of death and graft loss.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.763 | 0.118 |
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".