Systematic Review of Nonsteroidal Anti-Inflammatory Drug-Induced Adverse Effects in Dogs
Bibliographic record
Abstract
The aim of this systematic review was to identify, assess, and critically evaluate the quality of evidence of nonsteroidal anti-inflammatory drug (NSAID)-induced adverse effects in dogs. Original prospective studies published in peer-reviewed journals in English (1990-2012) that reported data on the safety of NSAIDs administration in dogs were searched. For each study, design type (I, II, III, or IV) and assessment of quality (+, Ø, -) were rated. For each drug, quantity and consistency rating (***, **, *) and strength of evidence (high, moderate, low, or extremely low) were identified and evaluated. The strength of evidence was defined in terms of how applicable and relevant the conclusions were to the target population. Sixty-four studies met the inclusion criteria. Thirty-five (55%) research studies and 29 (45%) clinical trials were identified. A high strength of evidence existed for carprofen, firocoxib, and meloxicam; moderate for deracoxib, ketoprofen, and robenacoxib; and low for etodolac. Quality and consistency rating were as follows: carprofen (***/***), deracoxib (**/***), etodolac (*/unable to rate), firocoxib (***/**), ketoprofen (**/***), meloxicam (***/***), and robenacoxib (**/**), respectively. Adverse effects were detected in 35 studies (55%) and commonly included vomiting, diarrhea, and anorexia. Three studies (5%) reported a power analysis related to adverse effects of ≥80%. In randomized, placebo-controlled, blinded studies (n = 25, 39%), the incidence of adverse effects was not statistically different between treated and control dogs. Finally, most studies were not appropriately designed to determine the safety of NSAIDs, and involved a healthy nongeriatric population of research dogs.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".