The difficult journey to treatment for women suffering from heavy menstrual bleeding: a multi-national survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it