Outcomes among CMV‐mismatched and highly sensitized kidney transplants recipients who develop neutropenia
Bibliographic record
Abstract
Limited data exist on the incidence and clinical outcomes of neutropenia among kidney transplant recipients. Our study included 572 adults who received a kidney transplant at the University of California, San Francisco Medical Center between 2012 and 2018, and were CMV-mismatched or had a PRA ≥ 80%. Recipients with HIV, Hepatitis B and C, and primary non-function were excluded. Participants were followed for at least 1 year after transplantation. Neutropenia was defined as absolute neutrophil count < 1000 cells/μl. Cox proportional hazards regression models using neutropenia as a time-varying predictor were used to determine the risk of mycophenolic acid and valganciclovir changes, rejection, hospitalizations and use of granulocyte colony stimulating factor. Models were adjusted for demographics and transplant characteristics. Mean follow-up was 3.7 (SD, 1.8) years. The mean age of the cohort was 50.4 (13.1) years, and 57.5% were female. A total of 208 (36.3%) participants had neutropenia. Neutropenia was associated with an increased risk of valganciclovir or MPA dose reductions or discontinuations [adjusted hazard ratio, aHR: 7.78, 95% CI: 4.73-12.81], rejection [aHR 2.00, 95% CI: 1.10-3.64] and hospitalizations [aHR 3.32, 95% CI: 2.12-5.19]. Neutropenia occurs frequently after kidney transplantation and leads to more medication changes and adverse clinical outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".