North American and European practices for opioid-sparing and opioid-free anaesthesia: a cross-sectional survey
Bibliographic record
Abstract
Background Opioids remain central to perioperative analgesia but concerns about the growing opioid crisis and adverse effects have prompted revaluation of their role. Opioid-sparing anaesthesia and opioid-free anaesthesia (OFA) have emerged as alternatives, yet their clinical adoption remains uncertain. This survey assessed adoption and perceptions among anaesthesiologists in North America and Europe. Methods A 26-question cross-sectional, web-based survey was distributed via email to members of the American, European, and French Societies of anaesthesiologists. The survey assessed routine use of opioid-sparing techniques, defined as the regular use of non-opioid analgesics and adjuncts to minimise intraoperative opioid use in the past month. We hypothesised that fewer than 50% of anaesthesiologists routinely used these techniques during this period. Results The overall response rate was 2% among ASA members (614/31 000) and 12% among European Society of Anaesthesiology and Intensive Care (ESAIC) members (414/3500). Concern about opioid use was reported as high in ESAIC and ASA members (90% vs 83%, P <0.001). Daily use of opioid sparing techniques was reported by 37% (95% confidence interval [CI] 32–42%) of ESAIC and 40% (95% CI 36–45%) of ASA members. OFA use was less common overall but reported to be higher by ASA repondents (21%, 95% CI 18–25%) vs 12% (95% CI 9–15%), P <0.001) for EASIC respondents. Perceived risks differed: EASIC respondents more often cited haemodynamic instability (43% vs 16%, P <0.001), whereas ASA respondents more often cited patient dissatisfaction (55% vs 30%) and uncontrolled pain (72% vs 53%, both P <0.001). Key barriers to OFA adoption included limited training, low confidence, and lack of evidence-based guidelines. Conclusions Interest in opioid-sparing anaesthesia and OFA is widespread, but routine use remains modest and varies by region. Regional perceptions, institutional protocols, and confidence in evidence appear to influence implementation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".