Evolving the management of acute perioperative pain towards opioid free protocols: a narrative review
Bibliographic record
Abstract
Objective: Identification of pain as the fifth vital sign has resulted in over-prescription and overuse of opioids in the US, with addiction reaching epidemic proportions. In Europe, and more recently in the US, a shift has occurred with the global adoption of multimodal analgesia (MMA), which seeks to minimize perioperative opioid use. Improved functional outcomes and reduced healthcare utilization costs have been demonstrated with MMA, but wide scale use of opioids in pain management protocols continues. As a next step in the pain management evolution, opioid-free analgesia (OFA) MMA strategies have emerged as feasible in many surgical settings.Methods: Articles were limited to clinical studies and meta-analyses focusing on comparisons between opioid-intensive and opioid-free/opioid-sparing strategies published in English.Results: In this review, elimination or substantial reduction in opioid use with OFA strategies for perioperative acute pain are discussed, with an emphasis on improved pain control and patient satisfaction. Improved functional outcomes and patient recovery, as well as reduced healthcare utilization costs, are also discussed, along with challenges facing the implementation of such strategies.Conclusions: Effective MMA strategies have paved the way for OFA approaches to postoperative pain management, with goals to reduce opioid prescriptions, improve patient recovery, and reduce overall healthcare resource utilization and costs. However, institution-wide deployment and adoption of OFA is still in early stages and will require personalization and better management of patient expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".