Opioids and Orthopaedics in North America: Addressing a Growing Concern
Bibliographic record
Abstract
Pain relief is easier to discuss when we acknowledge that pain intensity varies substantially for a given nociception, the physiology of actual or potential tissue damage. Variations in pain, i.e., the cognitive, emotional, and behavioral responses to nociception, are due to variations in psychosocial factors (mindset and circumstances). Musculoskeletal surgery is often palliative, discretionary, and preference-sensitive: settings in which psychosocial factors have a substantial influence on choices and health. Using opioid analgesics more liberally and downplaying their potential for misuse contributed to the current crisis of misuse and overdose deaths. However, pain relief should not depend on opioids. For instance, one of us (D.R.) documented that people in the Netherlands used no or nearly no opioids after ankle fracture surgery versus patients in the U.S. and had comparable pain relief1. Furthermore, inpatients in U.S. hospitals who take more opioids after surgery experience greater pain after similar operations2,3. We also found that self-efficacy, or the confidence that one can manage and recover, and positive mood are the most effective pain relievers2-4. Symptoms of depression or posttraumatic stress are often inadvertently treated with unwarranted refills of opioid prescriptions in lieu of less risky and more effective treatments, such as cognitive behavioral therapy5. In the article “Support for Safer Opioid Prescribing Practices: A Catalog of Published Use After Orthopaedic Surgery” in this issue of The Journal of Bone & Joint Surgery, Lovecchio et al. summarize published data on postoperative opioid use after common orthopaedic procedures. The authors provide averages of opioid consumption, revealing a wide range of usage among patients. There are quite a few people in the U.S. and Canada who take a minimal quantity of opioids after surgery. We propose that the orthopaedic community consider this catalog to be a wake-up call, alerting us that we often overprescribe opioids after injury or surgery. The diversion of unused pills has played a major role in the current opioid crisis. By treating the outliers, often for our own convenience, we have contributed to the current national predicament. A catalog of opioid use in North American centers carries additional risk. If most of the world effectively manages pain with fewer opioids, such a catalog might adversely influence other countries’ safer, and perhaps more effective, pain management strategies. Over the years, we have adopted several useful strategies to minimize the use of opioid analgesics in clinical practice, and invite you to add yours to this list: Use nonopioid alternatives as a first line of pain medication following orthopaedic procedures. Prescribe the minimum necessary quantity of oral opioids for a limited period of time. Avoid prescribing long-acting preparations of oral opioids for acute pain of injury or surgery. Establish practicewide strategies for prescribing opioid analgesics, to depersonalize discussions with patients and their caregivers about limiting the use of opioids. Be mindful of requests for a greater amount or duration of opioids, as these may indicate opportunities to partner with other experts to address psychosocial stress using other approaches. Before elective surgery, discuss and individualize pain management strategies for more effective pain relief, using as few opioid pills as possible. Screen and address the potential for misuse and factors associated with greater pain intensity for a given nociception, including greater symptoms of depression or less effective coping strategies such as catastrophic thinking. Call patients the day after their ambulatory surgery or hospital discharge, to support and coach them in their recovery. Avoid prescribing a large number of opioid pills for extended durations and minimize the potential for overuse by utilizing electronic prescribing to enable smaller prescriptions and to more closely monitor patients’ opioid intake. Governmental6,7 and professional8,9 organizations are attempting to address the opioid crisis in the U.S. Together, we can help people to be more comfortable after injury or surgery and alleviate our patients’ postoperative pain safely and effectively. In doing so, we can also help to prevent the opioid crisis from spreading to other countries, and encourage clinicians and researchers to invest in alternative, nonopioid-based pain relief strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".