Mistaken Administration of Hydroxyurea Instead of Tacrolimus in a Pediatric Kidney Transplant Recipient: A Case Report
Bibliographic record
Abstract
Tacrolimus is a core medication of anti-rejection regimens for pediatric kidney transplant recipients. It is well known to have a narrow therapeutic window, affected by multiple factors, including absorption differences that influence drug concentrations in the body. Genetic polymorphisms of metabolizing enzymes, drug interactions, and intercurrent illnesses can impact drug clearance. This case report discusses a unique situation where a 12-year-old kidney transplant recipient experienced undetectable concentrations of tacrolimus in whole blood that were initially thought to result from excessive clearance due to severe diarrhea. Our nurse case manager investigated potential pharmacy errors by recommending that we test the home medication bottle, which revealed the absence of tacrolimus. Instead, the patient was given hydroxyurea, an anti-metabolite commonly used for oncologic and hematologic indications and can cause diarrhea, thrombocytopenia, and neutropenia. The patient experienced borderline rejection. The cause of this pharmacy error was multifactorial. However, confusion and complexities in the compounding process of liquid formulations likely played a role. This report underscores the importance of considering pharmacy errors as a potential cause of variations in tacrolimus drug concentrations. It emphasizes the role of nurses and interdisciplinary collaboration in identifying and addressing medication errors. It also underscores the need for standardization in the pharmaceutical compounding process and advocates for pharmaceutical industries to produce pediatric-appropriate formulations to reduce such errors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| 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".