Addressing the Opioid Crisis One Surgical Patient at a Time: Outcomes of a Novel Perioperative Pain Program
Bibliographic record
Abstract
Opioid prescriptions in the surgical setting have been implicated as contributors to the opioid epidemic. The authors hypothesized that a multidisciplinary approach to perioperative pain management for patients on chronic opioid therapy could decrease postoperative opioid requirements while reducing postoperative pain scores and improving functional outcomes. Therefore, a Perioperative Pain Program (PPP) for chronic opioid users was implemented. This study presents outcomes from the first 9 months of the PPP. Sixty-one patients met the inclusion criteria. Opioid consumption in morphine milligram equivalent (MME) was calculated and physical and health status of patients was assessed with the Brief Pain Inventory, Short-Form McGill Pain Questionnaire, and Short Form-12. Preliminary results showed significant reduction in MME, improved pain scores, and improved function for surgical patients on chronic opioids. PPP effectively reduced opioid usage without negatively influencing patient-reported outcomes, such as physical pain score assessment and health-related quality of life.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".