Management of heavy menstrual bleeding in women with bleeding disorders in a tertiary care center
Bibliographic record
Abstract
Background: Heavy menstrual bleeding (HMB) affects a significant number of women with bleeding disorders and has a negative impact on their quality of life. Objective: This retrospective study examined the management of patients with inherited bleeding disorders who used medical treatments, alone or in combination, for HMB. Methods: Chart review was performed on women attending the Women with Bleeding Disorders Clinic in Kingston, Ontario, between 2005 and 2017. Data collected included patient demographics, the reason for presentation and diagnosis, medical history, treatments, and patient satisfaction. Results: One hundred nine women were included in this cohort. Of these, only 74 (68%) were satisfied with medical management, and only 18 (17%) with first-line therapy. Treatments included combined contraceptives (oral pill, transdermal patch, and vaginal ring), progesterone-only pills, tranexamic acid, 52-mg levonorgestrel intrauterine system (LIUS), depomedroxyprogesterone acetate, and desmopressin, either alone or in combination. Satisfactory control of HMB occurred most often with the LIUS. Conclusion: In this cohort, managed in a tertiary care Women with Bleeding Disorders Clinic, only 68% of patients had successful control of HMB with medical treatment, and a minority were satisfied with first-line therapy. These data clearly highlight the need for additional research, including treatment approaches and novel therapies for this population.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".