Is otologic surgery contributing to the opioid epidemic?
Bibliographic record
Abstract
BACKGROUND: The opioid epidemic is a significant public health crisis challenging the lives of North Americans. Interestingly, this problem does not exist to the same extent in Europe. Surgeons play a significant role in prescribing opioids, especially in the context of post-operative pain management. The aim of this study was to compare the post-surgical prescribing patterns of otologists comparing Canada and Austria. METHODS: An online questionnaire was sent to 33 Canadian and 32 Austrian surgeons, who perform otologic surgery on a regular basis. Surgeons were asked to answer some questions about their background as well as typical prescribing patterns for postoperative pain medication for different ear surgeries (cochlear implant, stapedotomy, tympanoplasty). In addition, surgeons were asked about the typical use of local anesthetics for pain control at the end of a procedure. Otologists gave an estimate how confident they were that their therapy and prescriptions provide sufficient pain control to their patients. RESULTS: Analysis of the returned questionnaires showed that opioids are more commonly prescribed in Canada than in Austria. Nonsteroidal anti-inflammatory drugs are used for postoperative pain more regularly after ear surgery in Austria, as opposed to Canada. Some of the prescribed drugs by European otologists are not available in North America. The use of local anesthetics at the end of surgery is not common in Austria. Surgeons´ confidence that the prescribed pain medication was sufficient to control postoperative symptoms was higher in the group not prescribing opioids than in the group that did routinely prescribe opioids. CONCLUSION: Prescribing patterns differ substantially between the two evaluated countries. This data suggests an opportunity to reduce opioid prescriptions after otologic surgeries. Studies to evaluate pain after these operations as well as efficacy of analgesics following ear surgery are an important next step.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".