Opioid-free general anesthesia: considerations, techniques, and limitations
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the role of opioids during general anesthesia and examine their advantages and risks in the context of clinical practice. We define opioid-free anesthesia (OFA) as the absolute avoidance of intraoperative opioids. RECENT FINDINGS: In most minimally invasive and short-duration procedures, nonopioid analgesics, analgesic adjuvants, and local/regional analgesia can significantly spare the amount of intraoperative opioid needed. OFA should be considered in the context of tailoring to a specific patient and procedure, not as a universal approach. Strategies considered for OFA involve several adjuncts with low therapeutic range, requiring continuous infusions and resources, with potential for delayed recovery or other side effects, including increased short-term and long-term pain. No evidence indicates that OFA leads to decreased long-term opioid-related harms. SUMMARY: Complete avoidance of intraoperative opioids remains questionable, as it does not necessarily ensure avoidance of postoperative opioids. Multimodal analgesia including local/regional anesthesia may allow OFA for selected, minimally invasive surgeries, but further research is necessary in surgeries with high postoperative opioid requirements. Until there is definitive evidence regarding procedure and patient-specific combinations as well as the dose and duration of administration of adjunct agents, it is imperative to practice opioid-sparing approach in the intraoperative period.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".