Non‐steroidal anti‐inflammatory analgesics: a review of current practice
Bibliographic record
Abstract
Abstract Objective:This review is intended to update the reader on the clinical aspects of the non‐steroidal anti‐inflammatory analgesics (NSAIAs) currently used in veterinary practice. Most NSAIAs act by selectively, or preferentially, inhibiting the synthesis of cyclooxygenase (COX)‐1 or COX‐2, or both COX‐1 and COX‐2 enzymes which oxidize arachadonic acid to a series of prostenoids. The prostenoids are precursors of various prostaglandins required for many homeostatic functions throughout the body. The COX‐1 and COX‐2 enzymes are constitutive, however the COX‐2 enzyme is also induced following injury or inflammation facilitating the transmission of pain. As the newer NSAIAs focus on the inhibition of COX‐2, this review offers a more detailed discussion of this enzyme. Data synthesis:This data was obtained from recent review articles and original published reports in both the veterinary and human literature. A CAB and Medline search was also used. Conclusions:The NSAIAs are effective analgesics for managing moderate to severe pain in many species of animals; however, potential adverse effects may occur if used inappropriately. Guidelines, including indications, contraindications and dosing regimens for the commonly available NSAIAs are included.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".