One-year opioid consumption trajectories among individuals receiving multidisciplinary postsurgical care: a single-center observational study from the Toronto General Hospital Transitional Pain Service
Bibliographic record
Abstract
INTRODUCTION: The Transitional Pain Service (TPS) is an innovative, personalized approach to postsurgical opioid consumption and pain management. The objectives of this study were to identify trajectories of opioid consumption and pain intensity within 12 months after initiating treatment through the TPS, identify biopsychosocial factors associated with trajectory membership, and examine the relationship between trajectory membership and other outcomes of interest over the same 12-month period. METHODS: Consecutive patients referred to the TPS were included in the present study (n=466). After providing informed consent, they completed self-report questionnaires at the initial visit at the TPS (either pre surgery or post surgery) and at every TPS visit until 12 months. Growth mixture modeling was used to derive trajectories and identify associated factors. RESULTS: Results showed three distinct opioid consumption trajectories for both presurgical opioid consumers and opioid-naïve patients. These trajectories all decreased over time and among those who were consuming opioids before surgery that returned to presurgical levels. Being man, having a substance use disorder, or reporting higher levels of pain interference were associated with higher daily opioid consumption for presurgical opioid consumers. For presurgical opioid-naïve individuals, higher opioid consumption trajectories were associated with higher levels of psychological distress. Five pain intensity trajectories were identified, and there were no significant association between opioid consumption and pain intensity trajectories. CONCLUSIONS: Results suggest that opioid consumption and pain intensity trajectories mostly decrease after surgery in a high-risk population enrolled in a TPS. Results also show heterogeneity in postsurgical recovery and highlight the importance of using personalized interventions to optimize individual trajectories.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".