Impact of a Transitional Pain Service on postoperative opioid trajectories: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: It has been well described that a small but significant proportion of patients continue to use opioids months after surgical discharge. We sought to evaluate postdischarge opioid use of patients who were seen by a Transitional Pain Service compared with controls. METHODS: We conducted a retrospective cohort study using administrative data of individuals who underwent surgery in Ontario, Canada from 2014 to 2018. Matched cohort pairs were created by matching Transitional Pain Service patients to patients of other academic hospitals in Ontario who were not enrolled in a Transitional Pain Service. Segmented regression was performed to assess changes in monthly mean daily opioid dosage. RESULTS: A total of 209 Transitional Pain Service patients were matched to 209 patients who underwent surgery at other academic centers. Over the 12 months after surgery, the mean daily dose decreased by an estimated 3.53 morphine milligram equivalents (95% CI 2.67 to 4.39, p<0.001) per month for the Transitional Pain Service group, compared with a decline of only 1.05 morphine milligram equivalents (95% CI 0.43 to 1.66, p<0.001) for the controls. The difference-in-difference change in opioid use for the Transitional Pain Service group versus the control group was -2.48 morphine milligram equivalents per month (95% CI -3.54 to -1.43, p=0.003). DISCUSSION: Patients enrolled in the Transitional Pain Service were able to achieve opioid dose reduction faster than in the control cohorts. The difficulty in finding an appropriate control group for this retrospective study highlights the need for future randomized controlled trials to determine efficacy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".