Postoperative Opioid Administration and Prescription Practices Following Hysterectomy in Two Tertiary Care Centres: A Comparative Cohort Study between Canada and Austria
Bibliographic record
Abstract
Background: In light of the opioid epidemic, opioid-prescribing modalities for postoperative pain management have been discussed controversially and show a wide variation across geographic regions. The aim of this study was to compare postoperative pain treatment regimes. Methods: We performed a matched cohort study of women undergoing hysterectomy in Austria (n = 200) and Canada (n = 200). We aimed to compare perioperative opioid medications, converted to morphine equivalent dose (MED) and doses of non-opioid analgesic (NOA) within the first 24 h after hysterectomy, and opioid prescriptions at discharge between the two cohorts. Results: The total MED received intraoperatively, in the post-anaesthesia care unit (PACU) and during the first 24 h after surgery, was similar in both cohorts (145.59 vs. 137.87; p = 0.17). Women in the Austrian cohort received a higher MED intraoperatively compared to the Canadian cohort (117.24 vs. 79.62; p < 0.001) but a lower MED in the PACU (25.96 vs. 30.42; p = 0.04). The primary outcome, MED within 24 h in the postoperative ward, was markedly lower in the Austrian compared to the Canadian cohort (2.36 vs. 27.98; p < 0.001). In a regression analysis, only the variables “Country” and “mode of hysterectomy” affected this outcome. A total of 98.5% in the Canadian cohort were given an opioid prescription at discharge vs. 0% in the Austrian cohort. Conclusions: Our analysis reveals marked differences between Austria and Canada regarding pain management practices following elective hysterectomy; the significantly higher intraoperative and significantly lower postoperative MED administration in the Austrian cohort compared to the Canadian cohort seems to be significantly affected by each country’s cultural attitudes towards pain management; this may have significant public health consequences and warrants further research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".