Health issues in women and girls affected by haemophilia with a focus on nomenclature, heavy menstrual bleeding, and musculoskeletal issues
Bibliographic record
Abstract
INTRODUCTION: Women and girls affected by haemophilia, including haemophilia carriers (WGH) are at risk of bleeding symptoms that may go unrecognized, including heavy menstrual bleeding (HMB) and musculoskeletal bleeding. Terminology continues to evolve. AIM: To describe the current recommendations for nomenclature surrounding WGH, and the current understanding of HMB, iron deficiency, and musculoskeletal complaints in these patients. METHODS: Literature was reviewed and summarized. RESULTS: With regards to nomenclature, women with factor levels less than 50% should be classified as having haemophilia, while carriers with normal levels should be characterized accordingly to symptomatology. HMB and resultant iron deficiency are common among WGH, have a multitude of downstream effects, and maybe overlooked due to stigma around menstruation. Musculoskeletal bleeding and resultant joint changes are increasingly recognized in this population but do not necessarily correlate with factor levels. CONCLUSION: Although progress has been made in the care of WGH, much work remains to further improve their care.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".