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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".