Clinical Management of Pain in Rodents
Bibliographic record
Abstract
The use of effective regimens for mitigating pain remain underutilized in research rodents despite the general acceptance of both the ethical imperative and regulatory requirements intended to maximize animal welfare. Factors contributing to this gap between the need for and the actual use of analgesia include lack of sufficient evidence-based data on effective regimens, under-dosing due to labor required to dose analgesics at appropriate intervals, concerns that the use of analgesics may impact study outcomes, and beliefs that rodents recover quickly from invasive procedures and as such do not need analgesics. Fundamentally, any discussion of clinical management of pain in rodents must recognize that nociceptive pathways and pain signaling mechanisms are highly conserved across mammalian species, and that central processing of pain is largely equivalent in rodents and other larger research species such as dogs, cats, or primates. Other obstacles to effective pain management in rodents have been the lack of objective, science-driven data on pain assessment, and the availability of appropriate pharmacological tools for pain mitigation. To address this deficit, we have reviewed and summarized the available publications on pain management in rats, mice and guinea pigs. Different drug classes and specific pharmacokinetic profiles, recommended dosages, and routes of administration are discussed, and updated recommendations are provided. Nonpharmacologic tools for increasing the comfort and wellbeing of research animals are also discussed. The potential adverse effects of analgesics are also reviewed. While gaps still exist in our understanding of clinical pain management in rodents, effective pharmacologic and nonpharmacologic strategies are available that can and should be used to provide analgesia while minimizing adverse effects. The key to effective clinical management of pain is thoughtful planning that incorporates study needs and veterinary guidance, knowledge of the pharmacokinetics and mechanisms of action of drugs being considered, careful attention to individual differences, and establishing an institutional culture that commits to pain management for all species as a central component of animal welfare.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".