Postoperative Opioid Prescribing: Finding the Balance
Bibliographic record
Abstract
Editorial| August 2022 Postoperative Opioid Prescribing: Finding the Balance This article has an Audio Podcast Daniel I. McIsaac, M.D., M.P.H., F.R.C.P.C.; Daniel I. McIsaac, M.D., M.P.H., F.R.C.P.C. 1Departments of Anesthesiology & Pain Medicine, University of Ottawa and Ottawa Hospital, Ottawa, Canada; School of Epidemiology & Public Health, University of Ottawa, Ottawa, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada. https://orcid.org/0000-0002-8543-1859 Search for other works by this author on: This Site PubMed Google Scholar Karim S. Ladha, M.D., M.Sc. Karim S. Ladha, M.D., M.Sc. 2Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Canada; Department of Anesthesiology and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada. Search for other works by this author on: This Site PubMed Google Scholar Author and Article Information Accepted for publication June 13, 2022. This editorial accompanies the article on p. 151. This article has a related Infographic on p. A17. Address correspondence to Dr. McIsaac: Anesthesiology August 2022, Vol. 137, 131–133. https://doi.org/10.1097/ALN.0000000000004297 Connected Content Article: Surgeon Variation in Perioperative Opioid Prescribing and Medium- or Long-term Opioid Utilization after Total Knee Arthroplasty: A Cross-sectional Analysis Infographic: The Early Days: Do Immediate Perioperative Opioid Prescribing Practices Affect Long-Term Opioid Utilization? Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Cite Icon Cite Get Permissions Search Site Citation Daniel I. McIsaac, Karim S. Ladha; Postoperative Opioid Prescribing: Finding the Balance. Anesthesiology 2022; 137:131–133 doi: https://doi.org/10.1097/ALN.0000000000004297 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsAnesthesiology Search Advanced Search Topics: opioids, prescribing behavior As many as 300 million surgical procedures are performed annually, making surgical therapy, and related perioperative management, a clear contributor to public and population health. Concurrently, management of the opioid epidemic has emerged as a top public health priority, especially in the United States and Canada, where per capita prescription opioid use substantially exceeds the global average. While any direct link between perioperative opioid prescribing and population-level opioid misuse is complex, as opioids are routinely prescribed for postoperative pain management, anesthesiologists can have a role to play in opioid stewardship. As previously discussed in detail in the journal, the relationship between opioid prescribing practices and public health implications of the opioid epidemic exists within a multifaceted ecosystem.1 Ultimately, the interplay between provider behaviors (driving opioid supply), patient behaviors (driving demand), and the disposal rate of unused opioids impacts the overall pool of opioids in our communities. Shrinking the size... You do not currently have access to this content.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".