Uterine Fibroid Embolization Survey in Canada: Challenges, Opportunities, and Differences in Practices Across the Country
Bibliographic record
Abstract
Purpose: To assess the current practices surrounding Uterine Fibroid Embolization (UFE) in Canada. Methods: An online survey was sent to Canadian Association for Interventional Radiology (CAIR) members. It included questions on symptoms prompting UFE, patient awareness, investigation, UFE settings, the number of UFE procedures, and post-UFE care. The findings were discussed at CAIR’s 2023 annual meeting by an expert panel. Results: Out of 792 surveys sent, 87 were filled (11%). Menorrhagia is the most common indication for UFE (87%). Women’s awareness of UFE as a treatment option for fibroids is viewed as poor or average by 94% of our survey respondents. Most respondents see patients in clinics (92%) before the procedure and evaluate fibroids with MRI pre-UFE (76%). There is variability in care post-UFE, with 33% of procedures being performed as day surgery while 67% lead to overnight stay. For pain management, intravenous analgesia (including patient-controlled analgesia) is used in 76% (63/83) of cases while 19% (16/83) of respondents mentioned using epidural analgesia. Finally, there is an even split between embolic agent used; non-spherical polyvinyl alcohol (50%) and spherical particles (50%). Conclusion: Respondents believe patients in Canada still have limited awareness of UFE. Interventional radiologists are increasingly involved in the entire patient care trajectory, overseeing pre-and post-procedure care and hospitalizing patients. For pain management after UFE, it is observed that while epidural analgesia has been demonstrated more effective than alternatives, it is not widely used as the primary method.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".