Hyperalgesia and increased sevoflurane minimum alveolar concentration induced by opioids in the rat
Bibliographic record
Abstract
BACKGROUND: Perioperative opioids reduce inhalational anaesthetic requirements. The initial hypoalgesia may, however, be followed by a rebound hyperalgesia. OBJECTIVES: To determine whether prior opioid administration influences inhalational anaesthetic requirements, which might be associated with opioid-induced hyperalgesia. DESIGN: A prospective, randomised, experimental study. SETTING: Experimental Surgery, La Paz University Hospital, Madrid, Spain. ANIMALS: Seventy-nine adult male Wistar rats. INTERVENTIONS: Sevoflurane minimum alveolar concentration (MAC) and mechanical nociceptive thresholds (MNTs) were assessed at baseline and 7 days later following opioid treatment with remifentanil 120 μg kg-1 h-1, buprenorphine 150 μg kg-1, methadone 8 mg kg-1 or morphine 10 mg kg-1 The duration of the effect of remifentanil on MAC and MNT was evaluated in addition to the preventive effect of ketamine 10 mg kg-1 on remifentanil-induced hyperalgesia. MAIN OUTCOME MEASURES: The effect of different opioid treatments on MAC and MNT was evaluated using analysis of variance (ANOVA). RESULTS: All studied opioids produced an immediate reduction in sevoflurane MAC, followed by an increase (16%) in baseline MAC 7 days later (P < 0.05), although the immediate MAC reduction produced by these opioids at that time was not different. Remifentanil produced a decrease in MNT (P < 0.05), which was associated with an increase in the MAC (P < 0.05) that persisted at 21 days. The effect of remifentanil on MNT and MAC was blocked by ketamine. CONCLUSION: Opioid-induced hyperalgesia was associated with an increase in the MAC in normal rats who had not undergone surgery. Both effects lasted 21 days and were prevented by ketamine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".