Acute pain in cats: Recent advances in clinical assessment
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Pain assessment has gained much attention in recent years as a means of improving pain management and treatment standards. It has become an elemental part of feline practice with ultimate benefit to feline health and welfare. Currently pain assessment involves mostly the investigation of sensory-discriminative (intensity, location and duration) and affective-motivational (emotional) domains of pain. Specific behaviors associated with acute pain have been identified and constitute the basis for its assessment in cats. RECENT ADVANCES: The publication of pain scales with reported validation - the UNESP-Botucatu multidimensional composite pain scale and the Glasgow feline composite measure pain scale - and species-specific studies have advanced our knowledge on the subject. Facial expressions have also been shown to be different between painful and non-painful cats, and very recently the Feline Grimace Scale has been validated as a tool for acute pain assessment. CLINICAL CHALLENGES: Despite recent advances, several challenges still exist. For instance, the effects of disease and sedation on pain scoring/ assessment are unknown. Also, specific painful conditions (eg, dental pain) have not been systematically investigated. The development and validation of instruments for pain assessment by cat owners is warranted, as these tools are currently lacking. AIMS: This article reviews the use, advantages, disadvantages and limitations of the two validated pain scales, and presents a practical, stepwise approach to feline pain recognition and assessment using a dynamic and interactive process. The authors also offer perspectives regarding current challenges and future directions.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".