Tacrolimus-induced leukopenia in a kidney transplant recipient: A case study
Bibliographic record
Abstract
Introduction: Leukopenia occurs in 10%-55% of patients after kidney transplant and neutropenia occurs in approximately 28% of patients after kidney transplant. Resolution of leukopenia and neutropenia is done through treatment of the pathogen, such as cytomegalovirus, or removing the offending medication. Medications that are first thought to contribute to leukopenia include Valganciclovir, Sulfamethoxazole-Trimethoprim, and Mycophenolic Acid. Tacrolimus rarely contributes to leukopenia and neutropenia post kidney transplantation.Case presentation: This case study presents a patient who developed leukopenia and neutropenia 13 weeks after solitary kidney transplantation.Management: Mycophenolate Mofetil, Valganciclovir, Ergocalciferol, Aspirin, Famotidine, and Sulfamethoxazole-Trimethoprim were all discontinued. Filgrastim was used intermittently to increase white blood cell count. Ultimately, Tacrolimus was switched to Cyclosporine.Outcome: Leukopenia was resolved by switching Tacrolimus to Cyclosporine-based immunosuppression.Discussion: A systematic approach should be taken to resolve leukopenia post-kidney transplant. When a kidney transplant recipient is on Tacrolimus-based immunosuppression, Tacrolimus should be the last medication changed when attempting to resolve leukopenia.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".