Opioid weaning and pain management in postsurgical patients at the Toronto General Hospital Transitional Pain Service
Bibliographic record
Abstract
BACKGROUND: The perioperative period provides a critical window to address opioid use, particularly in patients with a history of chronic pain and presurgical opioid use. The Toronto General Hospital Transitional Pain Service (TPS) was developed to address the issues of pain and opioid use after surgery. AIMS: To provide program evaluation results from the TPS at the Toronto General Hospital highlighting opioid weaning rates and pain management of opioid-naïve and opioid-experienced surgical patients. METHODS: Two hundred fifty-one high-risk TPS patients were dichotomized preoperatively as opioid naïve or opioid experienced. Outcomes included pain, opioid consumption, weaning rates, and psychosocial/medical comorbidities. RESULTS: Six months postoperatively, pain and function were significantly improved. Opioid-naïve and opioid-experienced patients reduced consumption by 69% and 44%, respectively. Forty-six percent and 26% weaned completely. Consumption at hospital discharge predicted weaning in opioid-naïve patients. Pain catastrophizing, neuropathy, and recreational drug use predicted weaning in opioid-experienced patients. CONCLUSIONS: The TPS enabled almost half of opioid-naïve patients and one in four opioid-experienced patients to wean. The TPS successfully targets perioperative opioid use in complex pain patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".