Pharmacological strategies in multimodal analgesia for adults scheduled for ambulatory surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review aims to propose pharmacological strategies to enhance current clinical practices for analgesia in ambulatory surgical settings and in the context of the opioid epidemic. RECENT FINDINGS: Each year, a high volume of patients undergoes ambulatory surgery worldwide. The multimodal analgesia proposed to ambulatory patients must provide the best analgesic effect and patient satisfaction while respecting the rules of safety for ambulatory surgery. The role of nurses, anesthesiologists, and surgeons around said surgery is to relieve suffering, achieve early mobilization and patient satisfaction, and reduce duration of stay in hospital. Currently, and particularly in North America, overprescription of opioids has reached a critical level constituting a 'crisis'. Thus, we see the need to offer more optimal multimodal analgesia strategies to ambulatory patients. SUMMARY: These strategies must combine three key components when not contraindicated: regional/local analgesia, acetaminophen, and nonsteroidal anti-inflammatory drugs (NSAIDs). Adjuvants such as gabapentinoids, N-methyl-D-aspartate receptor modulators, glucocorticoids, α2-adrenergic receptor agonists, intravenous lidocaine might be added to the initial multimodal strategy, however, caution must be used regarding their side effects and risks of delaying recovery after ambulatory surgery. Weaker opioids (e.g. oxycodone, hydrocodone, tramadol) could be used rather than more powerful ones (e.g. morphine, hydromorphone, inhaled fentanyl, sufentanil). This, combined with education about postoperative weaning of opioids after surgery must be done in order to avoid long-term reliance of these drugs.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".