Seizures Associated With High-Dose Cefazolin in a Patient With Renal Dysfunction: A Case Report
Bibliographic record
Abstract
Introduction and Objective: Cefazolin-induced encephalopathy and seizures are possibly related to excessive dosing; especially in those with renal dysfunction. This report aims to highlight the importance of dose adjustments of cefazolin in patients with diminished renal function. Case Presentation: An 87-year-old female with a history of cognitive impairment, remote cerebellar infarcts, hypertension, and hypothyroidism presented with acute delirium associated with a urinary tract infection. Her condition worsened and she was found to have a methicillin-sensitive Staphylococcus aureus bacteremia for which she was started on cefazolin 2 grams intravenously every 4 hours. Based on her renal function, recommended dosing would have been 2 grams intravenously every 12 hours. After 3 days on this regimen her mentation declined and she suffered a tonic-clonic seizure. She did not regain consciousness and was transitioned to comfort care prior to her death. Discussion: Supratherapeutic dosing of cefazolin may have led to significant neurotoxic effects. Neurotoxicity and seizures can occur with drug accumulation from an increase in excitatory neurotransmitters along with a decrease in inhibitory neurotransmitter activity. The effect is potentiated by older age, pre-existing central nervous system conditions, and renal failure. Therapeutic drug monitoring is a potential strategy to limit the risk of drug toxicity. Conclusion: This case outlines a poor outcome in the context of high-dose cefazolin. It serves as a reminder to clinicians for ongoing pharmacovigilance in adhering to treatment guidelines.
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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 | 0.000 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".