Acceptance and Commitment Therapy to manage pain and opioid use after major surgery: Preliminary outcomes from the Toronto General Hospital Transitional Pain Service
Bibliographic record
Abstract
Background: Chronic postsurgical pain (CPSP) and associated long-term opioid use are major public health concerns.Aims: The Toronto General Hospital Transitional Pain Service (TPS) is a multidisciplinary, hospital-integrated program developed to prevent and manage CPSP and support opioid tapering. This clinical practice–based study reports on preliminary outcomes of the TPS psychology program, which provides acceptance and commitment therapy (ACT) to patients at risk for CPSP and persistent opioid use.Methods: Ninety-one patients received ACT, whereas 252 patients did not (no ACT group). Patient outcomes were compared for the two groups at first and last TPS visits. Pain, pain interference, sensitivity to pain traumatization, pain catastrophizing, anxiety, depression, and opioid use were analyzed using two-way (Group [ACT, no ACT] × Time [first, last visit]) analyses of variance (ANOVAs).Results: Patients referred to ACT were more likely to report a mental health condition preoperatively (P < 0.001), had higher opioid use (P < 0.001) at the first postsurgical visit, and reported higher sensitivity to pain traumatization (P < 0.05) and anxiety (P < 0.05) than the no ACT group at both time points. Both groups showed reductions in pain, pain interference, pain catastrophizing, anxiety, and opioid use by the last TPS visit (P < 0.05). The ACT group demonstrated greater reductions in opioid use and pain interference and showed reductions in depressed mood (P = 0.001) by the end of treatment compared to the no ACT group.Conclusion: Preliminary outcomes suggest that ACT was effective in reducing opioid use while pain interference and mood improved.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".