Analgesia for the pregnant, lactating and neonatal to pediatric cat and dog
Bibliographic record
Abstract
Abstract Objective: Very little information on the approach to analgesia in pregnant, nursing or very young animals is available in the veterinary literature. A review of the human and veterinary literature on the various analgesics available for use in this group of patients is presented. The unique physiological characteristics that must be considered when selecting analgesics is discussed. Etiology: As with mature cats and dogs, the origin and severity of pain in this group of animals may be similar; however, differences do exist. Diagnosis: The diagnosis and assessment of pain in pregnant and nursing animals is based on the problem at hand and is similar to other mature animals. The diagnosis in the very young, however, may be more challenging, but should be suspected based on history and clinical signs. Response to analgesic therapy is advised in all animals to confirm the presence and degree of pain. Therapy: Various analgesics and analgesic modalities are discussed with emphasis placed on preference and caution for each group. Prognosis: Management of pain is extremely important in all animals, but especially the very young, where a permanent hyperalgesic response to pain may exist with inadequate therapy. Inappropriate analgesic selection in pregnant and nursing bitches or queens may result in congenital abnormalities of the fetus or neonate. Inadequate analgesia in nursing bitches or queens may cause aggressive behavior toward the young.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".