Role of the Perioperative Surgical Home in Optimizing the Perioperative Use of Opioids
Bibliographic record
Abstract
Several federal agencies have recently noted that the United States is in the midst of an unprecedented "opioid epidemic," with an increasing number of opioid-related overdoses and deaths. Providers currently face 3 population-level, public health challenges in providing optimal perioperative pain care: (1) the continued lack of overall improvement in the excessive incidence of inadequately treated postoperative pain, (2) minimizing or preventing postoperative opioid-related side effects, and (3) addressing current opioid prescribing patterns, and the accompanying problematic surge in prescription opioid diversion, misuse, abuse, addiction, and overdose. In the Perioperative Surgical Home model, anesthesiologists and other pain medicine specialists are uniquely qualified and positioned to develop, implement, and coordinate a comprehensive perioperative analgesic plan, which begins with the formal preoperative patient assessment and continues throughout the postdischarge, convalescence period. The scope and practice of pain management within the Perioperative Surgical Home should thus (a) expand to include routine preoperative patient-level pain-risk stratification (including the chronic use of opioid and nonopioid analgesics), (b) address the multitude of biopsychosocial factors that contribute to interpatient pain variability, and (c) extend and be well coordinated across all 4 phases of the surgical pain experience (preoperative, intraoperative, postoperative, and postdischarge). Specifically, safe and effective perioperative pain management should include a plan of care that is tailored to the individual patient's underlying disease(s), presence of a chronic pain condition and preoperative use of opioids, and the specific surgical procedure-with evidence-based, multimodal analgesic regimens being applied in the vast majority of cases. An iteratively evolutionary component of an existing institutional Perioperative Surgical Home program can be an integrated Transitional Pain Service, which is modeled directly after the well-established prototype at the Toronto General Hospital in Ontario, Canada. This multidisciplinary, perioperative Transitional Pain Service seeks to modify the pain trajectories of patients who are at increased risk of (a) long-term, increasing, excessive opioid consumption and/or (b) developing chronic postsurgical pain. Like the Perioperative Surgical Home program in which it can be logically integrated, such a Transitional Pain Service can serve as the needed but missing linkage to improve the continuum of care and perioperative pain management for elective, urgent, and emergent surgery. Even if successfully and cost-efficiently embedded within an existing Perioperative Surgical Home, a new perioperative Transitional Pain Service will require additional resources.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".