A retrospective review of diagnosis and management of heavy menstrual bleeding and co-morbidities in patients seen in Young Women’s Blood Clinic
Bibliographic record
Abstract
Introduction: The prevalence of heavy menstrual bleeding (HMB) is estimated as high as 37% in adolescents and is associated with co-morbidities, such as bleeding disorders (BD), iron deficiency anemia (IDA) and mood disorders. The Young Women's Blood Clinic (YWBC) is a multidisciplinary clinic in Hamilton, Ontario staffed with a gynaecologist, hematologist and nurse to provide diagnosis and management of HMB. This study was conducted to evaluate the diagnosis and management of HMB along with co-morbidities such as BD and IDA in adolescents seen at YWBC. Methods: This is a retrospective cohort study between July 2017 and June 2021. Patient records were reviewed for demographics, laboratory parameters, management plans and outcomes. Results: One hundred and three new patients with HMB were seen in the monthly YWBC during the study period. Four patients were referred with pre-existing BDs (2 von Willebrand Disease (vWD), 1 factor XI deficiency, 1 factor VII deficiency), while 4/101 (3.9%) were newly diagnosed (4 vWD). On presentation, 38 (36.9%) had IDA, while 43 patients (41.7%) had iron deficiency (ID) alone. Sixty-eight patients were treated with oral iron, while 13 required IV iron and 15 required blood transfusions. Oral contraceptive pills are the most common management option for patients diagnosed with and without BDs. Fifty-five point eight percent of patients reported improvement in HMB after 1st line treatment, 12.8% with 2nd line, 11.6% with third line or more, while 26.7% of patients reported ongoing HMB or had missing data. Conclusion: HMB can be debilitating and requires coordinated multidisciplinary care to diagnose and manage adequately.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".