Current Practice Patterns, Challenges, and Need for Education in Performing and Reporting Advanced Pelvic US and MRI to Investigate Endometriosis: A Survey by the Canadian Association of Radiologists Endometriosis Working Group
Bibliographic record
Abstract
Purpose: The Canadian Association of Radiologists (CAR) Endometriosis Working Group developed a national survey to evaluate current practice patterns associated with imaging endometriosis using advanced pelvic ultrasound and MRI to inform forthcoming clinical practice guidelines for endometriosis imaging. Methods: The anonymous survey consisted of 36 questions and was distributed electronically to CAR members. The survey contained a mix of multiple choice, Likert scale and open-ended questions intended to collect information about training and certification, current practices and protocols associated with imaging endometriosis, opportunities for quality improvement and continuing professional development. Descriptive statistics were used to summarize the results. Results: Canadian radiologists were surveyed about their experience with imaging endometriosis. A total of 89 responses were obtained, mostly from Ontario and Quebec. Most respondents were community radiologists, and almost 33% were in their first five years of practice. Approximately 38% of respondents reported that they or their institution performed advanced pelvic ultrasound for endometriosis, with most having done so for less than 5 years, and most having received training during residency or fellowship. 70% of respondents stated they currently interpret pelvic endometriosis MRI, with most having 1-5 years of experience. Conclusion: Many radiologists in Canada do not perform dedicated imaging for endometriosis. This may be due to a lack of understanding of the benefits and limited access to training. However, dedicated imaging can improve patient outcomes and decrease repeated surgeries. The results highlight the importance of developing guidelines for these imaging techniques and promoting a multidisciplinary approach to endometriosis management.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".