Consensus Statement for the Prescription of Pain Medication at Discharge after Elective Adult Surgery
Bibliographic record
Abstract
This Consensus Statement provides recommendations on the prescription of pain medication at discharge from hospital for opioid-naïve adult patients who undergo elective surgery. It encourages health care providers (surgeons, anesthesiologists, nurses/nurse practitioners, pain teams, pharmacists, allied health professionals, and trainees) to (1) use nonopioid therapies and reduce the prescription of opioids so that fewer opioid pills are available for diversion and (2) educate patients and their families/caregivers about pain management options after surgery to optimize quality of care for postoperative pain. These recommendations apply to opioid-naïve adult patients who undergo elective surgery. This consensus statement is intended for use by health care providers involved in the management and care of surgical patients. A modified Delphi process was used to reach consensus on the recommendations. First, the authors conducted a scoping review of the literature to determine current best practices and existing guidelines. From the available literature and expertise of the authors, a draft list of recommendations was created. Second, the authors asked key stakeholders to review and provide feedback on several drafts of the document and attend an in-person consensus meeting. The modified Delphi stakeholder group included surgeons, anesthesiologists, residents, fellows, nurses, pharmacists, and patients. After multiple iterations, the document was deemed complete. The recommendations are not graded because they are mostly based on consensus rather than evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.223 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".