Multimodal Pain Management in Older Elective Arthroplasty Patients
Bibliographic record
Abstract
Background: Pain management after elective arthroplasty in older adults is complicated due to the risk of undertreatment of postoperative pain and potential adverse effects from analgesics, notably opioids. Using combinations of analgesics has been proposed as potentially beneficial to achieve pain control with lower opioid doses. Objective: We compared a multimodal pain protocol with a traditional one, in older elective arthroplasty patients, measuring self-rated pain, incidence of postoperative delirium, quantity and cost of opioid analgesics consumed. Methods: One hundred fifty-eight patients, 70 years and older, admitted to tertiary care for elective arthroplasty were prospectively assessed postoperative days 1–3. Patients received either traditional postoperative analgesia (acetaminophen plus opioids) or a multimodal pain protocol (acetaminophen, opioids, gabapentin, celecoxib), depending on surgeon preference. Self-rated pain, postoperative delirium, and time to achieve standby-assist ambulation were compared, as were total opioid doses and analgesic costs. Results: Despite receiving significantly more opioid analgesics (traditional: 166.4 mg morphine-equivalents; multimodal: 442 mg morphine equivalents; t = 10.64, P < .0001), there was no difference in self-rated pain, delirium, or mobility on postoperative days 1–3. Costs were significantly higher in the multimodal group ( t = 9.15, P < .0001). Knee arthroplasty was associated with higher pain scores than hip arthroplasty, with no significant difference in opioid usage. Conclusion: A multimodal approach to pain control demonstrated no benefit over traditional postoperative analgesia in elective arthroplasty patients, but with significantly higher amounts of opioid consumed. This poses a potential risk regarding tolerability in frail older adults and results in increased drug costs.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".