Cryo–Pneumatic Compression Results in a Significant Decrease in Opioid Consumption After Shoulder Surgery: A Multicenter Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The management of pain after shoulder surgery typically includes the use of cryotherapy and the prescription of opioid analgesics. Much focus has been placed lately on the opioid epidemic, which in part is fueled by excessive prescription of opioid medication. Previous studies have found a combination of cryotherapy and compression effective at reducing analgesic consumption and increasing recovery in patients undergoing knee and spine surgery; however, efficacy in patients undergoing shoulder surgery has not been evaluated. PURPOSE: To evaluate the effectiveness of a cryo-pneumatic compression device on postoperative shoulder pain, narcotic use, and quality of life when compared with standard care cryotherapy. STUDY DESIGN: Randomized controlled trial; Level of evidence, 2. METHODS: In total, 200 patients older than 18 years scheduled for unilateral shoulder surgery were enrolled. Patients were randomized to receive either postoperative cryo-pneumatic compression or standard care. The intervention group received a cryo-pneumatic device, while the standard care group received the treating surgeon's preferred method of postoperative care, including standard cryotherapy. Narcotic use was evaluated by the number of oral morphine milligram equivalents consumed during the postoperative period, as well as the time to cessation of narcotic use. Patient-reported outcome measures consisted of a numeric rating scale pain score, 36-item Short Form Survey, patient experience assessed using the net promoter score, and adverse events. Outcomes were evaluated at 2, 6, and 12 weeks postoperatively. RESULTS: = .0412). CONCLUSION: In patients undergoing unilateral shoulder surgery, the use of cryotherapy with pneumatic compression, when compared with standard care, resulted in significantly decreased opioid consumption as well as increased function at 2 weeks. REGISTRATION: NCT04185064 (ClinicalTrials.gov identifier).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".