Exploring Opioid-Sparing Multimodal Analgesia Options in Trauma: A Nursing Perspective
Bibliographic record
Abstract
Challenges with opioids (e.g., adverse events, misuse and abuse with long-term administration) have led to a renewed emphasis on opioid-sparing multimodal management of trauma pain. To assess the extent to which currently available evidence supports the efficacy and safety of various nonopioid analgesics and techniques to manage trauma pain, a literature search of recently published references was performed. Additional citations were included on the basis of authors' knowledge of the literature. Effective options for opioid-sparing analgesics include oral and intravenous (IV) acetaminophen; nonsteroidal anti-inflammatory drugs available via multiple routes; and anticonvulsants, which are especially effective for neuropathic pain associated with trauma. Intravenous routes (e.g., IV acetaminophen, IV ketorolac) may be associated with a faster onset of action than oral routes. Additional adjuvants for the treatment of trauma pain are muscle relaxants and alpha-2 adrenergic agonists. Ketamine and regional techniques play an important role in multimodal therapy but require medical and nursing support. Nonpharmacologic treatments (e.g., cryotherapy, distraction techniques, breathing and relaxation, acupuncture) supplement pharmacologic analgesics and can be safe and easy to implement. In conclusion, opioid-sparing multimodal analgesia addresses concerns associated with high doses of opioids, and many pharmacologic and nonpharmacologic options are available to implement this strategy. Nurses play key roles in comprehensive patient assessment; administration of patient-focused, opioid-sparing, multimodal analgesia in trauma; and monitoring for safety concerns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".