Adverse effects of rifampicin in dogs and serum alanine aminotransferase monitoring recommendations based on a retrospective study of 344 dogs
Bibliographic record
Abstract
BACKGROUND: Rifampicin has been reported to have potent activity against Staphylococci, including meticillin-resistant Staphylococcus pseudintermedius. There is limited documented information regarding adverse effects and recommendations for serum biochemistry monitoring. HYPOTHESIS/OBJECTIVES: The aims of this retrospective study were as follows: (i) to document the occurrence of adverse events in dogs receiving oral rifampicin; (ii) to determine the relationship between adverse events and the dosage/duration of therapy and concurrent medications; and (iii) to report findings associated with changes on serum alanine aminotransferase (ALT). ANIMALS: Client-owned dogs. METHODS: A retrospective review of 344 medical records was carried out. Serum ALT concentrations and adverse effects were recorded and analysed. Correlations between different time intervals (days 0-9, 10-18, 19-27, 28-36 and >36) and serum ALT elevation were compared. RESULTS: Dogs received 2.9-16 mg/kg/day of rifampicin. Adverse events occurred in 16.27% of dogs (56 of 344) and included vomiting (6.97%), anorexia (6.10%) and lethargy (3.77%). Adverse events were significantly more common in dogs concurrently treated with trimethoprim-sulfamethoxazole (P = 0.018), doxycycline (P = 0.044), levothyroxine sodium (P = 0.044), cephalosporins (P = 0.002) and nonsteroidal anti-inflammatories (P < 0.001). Twenty-five of 94 dogs (26.59%) had serum elevations of ALT. These increases were significantly associated with the duration of therapy during two time periods, 19-27 days (P = 0.04) and >36 days (P = 0.01). CONCLUSIONS AND CLINICAL IMPORTANCE: Significant adverse events were noted in association with concurrent drug administration and with serum ALT elevations. Pretreatment and weekly serum biochemistry monitoring is recommended to identify dogs at risk for hepatotoxicosis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".