Factors influencing serum concentrations of levetiracetam in dogs with epilepsy
Bibliographic record
Abstract
BACKGROUND: Factors affecting serum concentrations of levetiracetam in dogs are unknown and could affect the efficacy of levetiracetam in controlling seizures in dogs with epilepsy. HYPOTHESIS/OBJECTIVES: Higher PO doses of levetiracetam will be needed in dogs to achieve serum concentrations shown to be effective in humans. Determine factors that could influence serum levetiracetam concentrations and justify dose adjustment in some epileptic dogs. ANIMALS: Sixty-nine client-owned dogs with epilepsy treated with levetiracetam alone or in combination, based on 127 trough serum concentration measurements of levetiracetam. METHODS: Retrospective cohort study. Linear mixed models were used to assess the effect of patient signalment and concurrent drug administration on serum concentrations of levetiracetam and the effect of serum concentration of levetiracetam on seizure frequency reduction. RESULTS: = 0.30, P = .003). Based on our model, a levetiracetam dosage of 99-216 mg/kg/day is necessary to obtain a serum levetiracetam concentration of 20 μg/mL when used alone or concurrently with 7 mg/kg/day of phenobarbital. No other factors were found to influence serum levetiracetam concentrations. No therapeutic range could be identified. CONCLUSION AND CLINICAL IMPORTANCE: Our data suggest that a dosage of 99-216 mg/kg/day of levetiracetam is needed to achieve a serum concentration known to be therapeutically effective in humans, especially when administered concomitantly with phenobarbital.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".