Prevention and Management of Chronic Postsurgical Pain and Persistent Opioid Use Following Solid Organ Transplantation: Experiences From the Toronto General Hospital Transitional Pain Service
Bibliographic record
Abstract
BACKGROUND: With >700 transplant surgeries performed each year, Toronto General Hospital (TGH) is currently one of the largest adult transplant centers in North America. There is a lack of literature regarding both the identification and management of chronic postsurgical pain (CPSP) after organ transplantation. Since 2014, the TGH Transitional Pain Service (TPS) has helped manage patients who developed CPSP after solid organ transplantation (SOT), including heart, lung, liver, and renal transplants. METHODS: In this retrospective cohort study, we describe the association between opioid consumption, psychological characteristics of pain, and demographic characteristics of 140 SOT patients who participated in the multidisciplinary treatment at the TGH TPS, incorporating psychology and physiotherapy as key parts of our multimodal pain management regimen. RESULTS: Treatment by the multidisciplinary TPS team was associated with significant improvement in pain severity and a reduction in opioid consumption. CONCLUSIONS: Given the risk of CPSP after SOT, robust follow-up and management by a multidisciplinary team should be considered to prevent CPSP, help guide opioid weaning, and provide psychological support to these patients to improve their recovery trajectory and quality of life postoperatively.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".