Comparative effectiveness of opioid versus opioid-free analgesia after outpatient breast surgery: PAIN-Alt trial protocol
Bibliographic record
Abstract
INTRODUCTION: Excessive opioid prescribing after surgery can lead to adverse events and exacerbate the opioid crisis. Patients undergoing outpatient breast surgery are often prescribed opioids to manage pain at home; however, the value of this approach is uncertain. The Postoperative Analgesia Intervention with Non-opioid Alternatives (PAIN Alt) trial will address the following research question: among patients undergoing outpatient breast surgery, does opioid-free analgesia (OFA) result in non-inferior 7-day pain intensity and pain interference in comparison to opioid analgesia (OA)? METHODS AND ANALYSIS: This is a parallel, assessor-blind, open-label randomised trial conducted at seven university-affiliated hospitals in Canada. A sample of 540 adult patients (>18 years) undergoing outpatient mastectomy or lumpectomy will be included. Participants are allocated 1:1 to receive OA (around-the-clock non-opioids and opioids for breakthrough pain) or OFA (around-the-clock non-opioids, with adjustment of non-opioid drugs and/or non-pharmacological interventions for breakthrough pain). The co-primary outcomes are 7-day pain intensity and pain interference (measured using the Brief Pain Inventory). Secondary outcomes include adverse drug events, physical and mental health status, satisfaction with pain management, postoperative complications, chronic pain, opioid misuse, persistent opioid use, healthcare utilisation and costs. The primary statistical analyses will follow the intention-to-treat principle and be conducted using mixed-effects modelling. ETHICS AND DISSEMINATION: This trial is coordinated by the McGill University Health Centre (ethics approval MP-37-2024-102530), with ethics approval being sought at all participating sites. Our results will be published in an open-access, peer-reviewed journal, presented at relevant conferences and disseminated to the public through press releases. TRIAL REGISTRATION NUMBER: NCT06507345.
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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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.099 | 0.019 |
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".