Time to Stop Routinely Prescribing Opiates after Carpal Tunnel Release
Bibliographic record
Abstract
BACKGROUND: North American surgeons continue to routinely order narcotic medication for postoperative pain relief after carpal tunnel surgery. For some patients, this instigates persistent use. This double-blind, multicenter trial investigated whether over-the-counter medications were inferior to opioid pain control after carpal tunnel release. METHODS: Patients undergoing carpal tunnel release in five centers in Canada and the United States (n = 347) were randomly assigned to postoperative pain control with (opioid) hydrocodone/acetaminophen 5/325 mg versus over-the-counter ibuprofen/acetaminophen 600/325 mg. The two primary outcome measures were the Numeric Pain Rating Scale (0 to 10) and the six-item Patient-Reported Outcome Measurement Information System Pain Interference T-score. Secondary outcome measures were total medication used and overall satisfaction with pain medication management. RESULTS: The authors found no significant differences between opioid and over-the-counter patients in the Numeric Pain Rating Scale scores, Pain Interference T-scores, number of doses of medication, or patient satisfaction. The highest Numeric Pain Rating Scale group difference was the night of surgery, when opiate patients had 0.9/10 more pain than over-the-counter patients. The highest group difference in Pain Interference T-scores (2.1) was on the day of surgery, when the opiate patients had more pain interference than the over-the-counter group. Patient nationality or sex did not generate significant pain score differences. CONCLUSIONS: Pain management is not inferior for patients managed with over-the-counter acetaminophen/ibuprofen versus opioids. This study provides high-quality evidence that U.S. and Canadian surgeons should stop the routine prescription of narcotics after carpal tunnel surgery for patients who are not taking pain medicines daily before surgery. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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 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".