2024 ISFM and AAFP consensus guidelines on the long-term use of NSAIDs in cats
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Non-steroidal anti-inflammatory drugs (NSAIDs) are widely used and are effective for the management of pain in cats. These Guidelines will support veterinarians in decision-making around prescribing NSAIDs in situations of chronic pain, to minimise adverse effects and optimise pain management. Information is provided on mechanism of action, indications for use, screening prior to prescription, use in the presence of comorbidities, monitoring of efficacy, and avoidance and management of adverse effects. CLINICAL CHALLENGES: The cat's unique metabolism should be considered when prescribing any medications, including NSAIDs. Chronic pain may be challenging to detect in this species and comorbidities, particularly chronic kidney disease, are common in senior cats. Management of chronic pain may be complicated by prescription of other drugs with the potential for interactions with NSAIDs. EVIDENCE BASE: These Guidelines have been created by a panel of experts brought together by the International Society of Feline Medicine (ISFM) and American Association of Feline Practitioners (AAFP). Information is based on the available literature, expert opinion and the panel members' experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".