Neutropenia in kidney and liver transplant recipients: Risk factors and outcomes
Bibliographic record
Abstract
Abstract No studies have directly compared the key characteristics and outcomes of kidney (KTx) and liver transplantation (LTx) recipients with neutropenia. In this single‐center, retrospective, cohort study, we enrolled all adult patients who received a KTx or LTx between 2000 and 2011. Neutropenia was defined as 2 consecutive absolute neutrophil count (ANC) values <1500/mm3 in patients without preexisting neutropenia. The first neutropenia episode occurring during the first year post‐transplantation was analyzed. A total of 663 patients with KTx and 354 patients with LTx met the inclusion criteria. Incidence of neutropenia was 20% in KTx and 38% in LTx, respectively. High‐risk CMV status and valganciclovir (VGCV) use were significant predictors of neutropenia for KTx recipients, but only VGCV use vs nonuse in LTx recipients. Neutropenia was associated with worse survival in KTx recipients (adjusted HR 1.95, 95% CI 1.18‐3.22, P<.01), but not in LTx recipients (adjusted HR 0.75, 95% CI 0.52‐1.10, P=.15). Sixteen acute rejection episodes were associated with preceding neutropenia in KTx recipients (HR 1.77, 95% CI 1.16‐2.68, P=.007) and 24 acute rejection episodes in LTx recipients (HR 1.41, 95% CI 0.97‐2.04, P=.07). Incidence of infection was similar in patients with and without neutropenia among KTx and LTx 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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".