Predictors and Complications of Post Kidney Transplant Leukopenia
Bibliographic record
Abstract
BACKGROUND: Leukopenia occurs frequently following kidney transplantation and is associated with adverse clinical outcomes including increased infectious risk. In this study we sought to characterize the causes and complications of leukopenia following kidney transplantation. METHODS: ). Factors associated with post-transplant leukopenia were then included in a multivariable backwards stepwise selection process to create a prediction model for the outcome of interest. Cox regression analyses were subsequently used to determine if post-transplant leukopenia was associated with complications. RESULTS: Of 388 recipients, 152 (39%) developed posttransplant leukopenia. Factors associated with leukopenia included antithymocyte globulin as induction therapy (HR 3.32, 95% CI 2.25-4.91), valganciclovir (HR 1.84, 95% CI 1.25-2.70), tacrolimus (HR 3.05, 95% CI 1.08-8.55), prior blood transfusion (HR 1.17 per unit, 95% CI 1.09- 1.25), and donor age (HR 1.02 per year, 95% CI 1.00-1.03). Cytomegalovirus infection occurred in 26 patients with leukopenia (17.1%). Other than cytomegalovirus, leukopenia was not associated with posttransplant complications. CONCLUSION: Leukopenia commonly occurred posttransplant and was associated with modifiable and non-modifiable pretransplant factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".