Bibliographic record
Abstract
Analgesics are an integral part of routine pain management in mammals, yet their use in fish is still limited. Some recommendations on the use of analgesics in fish are currently in the literature; however, information on the properties of analgesic drugs in most fish species is still scarce and sometimes misleading. The present review of information on the use of analgesics in fish was thus compiled to help clinicians make an informed decision as to which drug and dose to use. The main agents that have been investigated are opioids, NSAID, and local anesthetics, primarily in rainbow trout and zebrafish. There is presently no overwhelming evidence of efficacy for most analgesics in fish, although beneficial effects on behavior and physiologic parameters have been reported in many instances, especially associated with morphine administration. Furthermore, most analgesics did not result in significant adverse side effects. Thus, analgesics could be administered whenever it is considered that an animal might experience pain, given that the drugs appear not to cause harm and may be beneficial. However, caution must be advised because 1) important interspecies variation has been reported and 2) unforeseen effects could affect experimental results. Further research is needed to investigate analgesic use in fish. This should be accompanied by research aimed at improving our knowledge of the various species of fish. The current lack of a validated approach to assessing pain in fish limits our ability to evaluate the efficacy of analgesics in fish.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.012 |
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".