Ten years of transitional pain service research and practice: where are we and where do we go from here?
Bibliographic record
Abstract
Chronic postsurgical pain (CPSP) is a prevalent yet unintended consequence of surgery with substantial burdens to the individual and their family, the healthcare system, and society at large. The present article briefly reviews the evidence for transitional pain services (TPSs) that have arisen in an effort to prevent and mange CPSP and persistent opioid use, and provides an update on recent novel risk factors for CPSP. Available evidence from one randomized controlled trial (RCT) and three non-randomized cohort studies suggests that TPS treatment is associated with better opioid use outcomes, including fewer opioid tablets prescribed at discharge, better opioid weaning results, a lower incidence of new-onset chronic opioid use, and lower consumption of opioids even at later time points up to 1 year after surgery. Another RCT indicates TPS treatment can be enhanced by provision of perioperative clinical hypnosis. While these preliminary studies are generally positive, large-scale, RCTs are needed to provide a more definitive picture of whether TPSs are effective in reducing opioid consumption and improving pain and mental health outcomes in the short and long term. With the expansion of TPSs across North America and globally, perioperative care focused on reducing the transition to pain chronicity has the potential to help millions of patients. With additional evidence from well-controlled RCTs, TPSs are well poised to continue to evolve and strengthen the role of multidisciplinary care teams in the immediate postdischarge period and beyond.
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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 | 0.080 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".