Management of abnormal uterine bleeding by northern, rural and isolated primary care physicians: PART II: What do we need?
Bibliographic record
Abstract
BACKGROUND: Abnormal uterine bleeding (AUB) is a common problem that affects one in five women during the pre-menopausal years. It is frequently managed by family physicians, especially in northern, rural and isolated areas where severe shortages of gynecologists exist. METHODS: We surveyed 194 family physicians in northern, rural and isolated areas of Ontario, Canada to determine their educational and resource needs for the management of AUB, with a specific focus on the relevance and feasibility of using clinical practice guidelines (CPGs). RESULTS: Most physicians surveyed did not use CPGs for the management of AUB because they did not know that such guidelines existed. The majority were interested in further education on the management of AUB through mailed CPGs and locally held training courses. A major theme among respondents was the need for more timely and effective gynecological referrals. CONCLUSION: A one-page diagnostic and treatment algorithm for AUB would be easy to use and would place minimal restrictions on physician autonomy. As the majority of physicians had Internet access, we recommend emailing and web posting in addition to mailing this algorithm. Local, hands-on courses including options for endometrial biopsy training would also be helpful for northern, rural and isolated physicians, many of whom cannot readily take time away from their practices.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".