Bibliographic record
Abstract
BACKGROUND: Erythrocytosis is relatively common post-kidney transplantation and may have adverse consequences. This study examined whether the incidence of erythrocytosis has remained stable over time and explored the impact of this condition on patient outcomes. METHODS: This was a retrospective single center review of an incidence cohort (transplanted between 1993 and 2005). Predictors of erythrocytosis and hemoglobin levels and subsequent patient and allograft survival were examined. RESULTS: Erythrocytosis (hemoglobin >170 g/L for >1 month) was observed in 59 of 511 recipients. Erythrocytosis developed in only 8.1% of those transplanted from 1997 to 2005, compared with 18.7% in those transplanted from 1993 to 1996 (p = 0.0005). Independent predictive factors were use of angiotensin converting enzyme inhibitors/angiotensin receptor blockers (ACEi/ARBs) (HR 0.176, 95% CI 0.040-0.71, p = 0.016), male gender (HR 3.72, 95% CI 1.54-9.0, p = 0.003), and mycophenolic acid agents (HR 0.49, 95% CI 0.237-0.99, p = 0.049). Patients with erythrocytosis had superior overall survival (HR for death 0.105, 95% CI 0.014-0.760, p = 0.026) but a trend for worse death censored graft loss (univariate HR 2.06, 95% CI 0.91-4.65, p = 0.084). CONCLUSIONS: The incidence of erythrocytosis is falling and is likely related to greater ACEi/ARB use and possibly more antiproliferative immunosuppression. Patient survival is excellent in those with erythrocytosis, but long-term graft survival may be compromised.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".