The difficult journey to treatment for women suffering from heavy menstrual bleeding: a multi-national survey
Bibliographic record
Abstract
PURPOSE: Up to 30% of women of reproductive age experience HMB, which has a substantial impact on their quality of life. A clinical care pathway for women with HMB is an unmet need, but its development requires better understanding of the factors that characterise current diagnosis and management of the condition. MATERIALS AND METHODS: This observational, survey-based study assessed the burden, personal experiences, and path through clinical management of women with HMB in Canada, the USA, Brazil, France and Russia using a detailed, semi-structured online questionnaire. After excluding those reporting relevant organic pathology, responses to the questionnaire from 200 women per country were analysed. RESULTS: Around 75% of women with HMB had actively sought information about heavy periods, mostly through internet research. The mean time from first symptoms until seeking help was 2.9 (Standard deviation, 3.1) years. However, 40% of women had not seen a health care professional about the condition. Furthermore, 54% had never been diagnosed or treated. Only 20% had been diagnosed and received appropriate treatment. Treatment was successful in 69% of those patients currently receiving treatment. Oral contraceptives were the treatment most commonly prescribed for HMB, although the highly effective levonorgestrel-intrauterine system was used by only a small proportion of women. CONCLUSIONS: This study provides insight into the typical journey of a woman with HMB which may help patients and health care professionals improve the path to diagnosis and treatment, although further research with long-term outcomes 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".