Chronic postsurgical pain: From risk factor identification to multidisciplinary management at the Toronto General Hospital Transitional Pain Service
Bibliographic record
Abstract
Background: Chronic postsurgical pain is a highly prevalent public health problem associated with substantial emotional, social, and economic costs.Aims: (1) To review the major risk factors for chronic postsurgical pain (CPSP); (2) to describe the implementation of the Transitional Pain Service (TPS) at the Toronto General Hospital, a multiprofessional, multimodal preventive approach to CPSP involving intensive, perioperative psychological, physical, and pharmacological management aimed at preventing and treating the factors that increase the risk of CPSP and related disability; and (3) to present recent empirical evidence for the efficacy of the TPS.Methods: The Toronto General Hospital TPS was specifically developed to target patients at high risk of developing CPSP. The major known risk factors for CPSP are perioperative pain, opioid use, and negative affect, including depression, anxiety, pain catastrophizing, and posttraumatic stress disorder–like symptoms. At-risk patients are identified early and provided comprehensive care by a multidisciplinary team consisting of pain physicians, advanced practice nurses, psychologists, and physical therapists.Results: Preliminary results from two nonrandomized, clinical practice–based trials indicate that TPS treatment is associated with improvements in pain, pain interference, pain catastrophizing, symptoms of anxiety and depression, and opioid use. Almost half of opioid-naïve patients and one in four opioid-experienced patients were opioid free by the 6-month point.Conclusions: These promising results suggest that the TPS benefits patients at risk of CPSP. A multicenter randomized controlled trial of the TPS in several Ontario hospitals is currently underway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".