Transition from acute pain service to transitional pain service
Bibliographic record
Abstract
Chronic postsurgical pain (CPSP), an often unanticipated result of necessary and even life–saving procedures, develops in 5–10% of patients one–year after major surgery. Sub–stantial advances have been made in identifying patients at elevated risk of developing CPSP based on perioperative pain, opioid use, and negative affect, including depression, anxiety, pain catastrophizing, and posttraumatic stress disorder–like symptoms. Toronto General Hospital (TGH) is the first to comprehensively address the problem of CPSP. Patients at high risk for CPSP are identified early and offered coordinated and comprehensive care by the multidisciplinary team consisting of pain physicians, advanced practice nurses, psychologists, and physiotherapists. Transitional pain service (TPS) has been effective in safely weaning patients from opioids in the postoperative period, in both opioid naive and experienced patients. At the same time, TPS involvement in the post–discharge period has also led to reductions in reported pain. With the increasing availability and adoption of mobile technology, the TPS has recently engaged patients in self–reporting of pain scores and functioning through mobile applications. This facilitates ongoing tracking and documentation, and allows patients to participate in their pain management more actively. The future of pain management must involve providing support and expertise to allow for the development of similar programs at other hospitals, allowing easier access for patients in need.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".