2022 ISFM Consensus Guidelines on the Management of Acute Pain in Cats
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Increases in cat ownership worldwide mean more cats are requiring veterinary care. Illness, trauma and surgery can result in acute pain, and effective management of pain is required for optimal feline welfare (ie, physical health and mental wellbeing). Validated pain assessment tools are available and pain management plans for the individual patient should incorporate pharmacological and non-pharmacological therapy. Preventive and multimodal analgesia, including local anaesthesia, are important principles of pain management, and the choice of analgesic drugs should take into account the type, severity and duration of pain, presence of comorbidities and avoidance of adverse effects. Nursing care, environmental modifications and cat friendly handling are likewise pivotal to the pain management plan, as is a team approach, involving the cat carer. CLINICAL CHALLENGES: Pain has traditionally been under-recognised in cats. Pain assessment tools are not widely implemented, and signs of pain in this species may be subtle. The unique challenges of feline metabolism and comorbidities may lead to undertreatment of pain and the development of peripheral and central sensitisation. Lack of availability or experience with various analgesic drugs may compromise effective pain management. EVIDENCE BASE: These Guidelines have been created by a panel of experts and the International Society of Feline Medicine (ISFM) based on the available literature and the authors' experience. They are aimed at general practitioners to assist in the assessment, prevention and management of acute pain in feline patients, and to provide a practical guide to selection and dosing of effective analgesic agents.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".