Management of abnormal uterine bleeding by northern, rural and isolated primary care physicians: PART I – How are we doing?
Bibliographic record
Abstract
BACKGROUND: Canadian hysterectomy rates have declined in recent years. However, hysterectomy rates for discretionary indications, principally abnormal uterine bleeding (AUB), remain high in some regions. In northern Ontario, hysterectomy rates for women aged 34-45 are almost triple the rates in southern, urban areas. Primary care physicians (family doctors) usually manage AUB initially in these northern areas where a severe shortage of gynecologists exists. METHODS: We surveyed 194 family physicians in northern Ontario with a case scenario of a pre-menopausal woman with heavy vaginal bleeding to characterize management and to gain physicians' perspectives on the factors that affect it. RESULTS: To investigate her heavy vaginal bleeding, only 17% of physicians recommended a pelvic examination for the woman in our case scenario. Most physicians advocated a course of medical therapy before referral to a gynecologist, for whom the average waiting time was seven weeks. However, most physicians recommended referral after only one failed trial of medical treatment. Physicians felt that major deterrents to medical treatments were patient desires for immediate relief and/or permanent solutions, poor patient compliance and the high cost of medication. Only 25% of respondents indicated that they would perform an endometrial biopsy prior to referral. CONCLUSIONS: Family physicians would benefit from further education on appropriate investigations for AUB, primarily training in pelvic examination and endometrial biopsy techniques, as well as appropriate treatment algorithms. Further research into patient perspectives on treatment options is needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".