Women and girls with haemophilia: A retrospective cohort study in China
Bibliographic record
Abstract
INTRODUCTION: Women and girls with haemophilia (WGH) may have spontaneous/traumatic bleeding similar to that in males with haemophilia, and in addition excessive bleeding during menstruation and delivery. AIM: To characterize WGH in China and provide guidance for better management. METHODS: We retrospectively analysed the characteristics of WGH registered in the Haemophilia Treatment Center Collaborative Network of China (HTCCNC) Registry, including demographics, diagnosis and treatment, bleeding characteristics, obstetrical and gynaecological experiences, and surgical history. RESULTS: A total of 61 females had confirmed haemophilia. Diagnosis and treatment were typically delayed, longer in mild haemophilia than in severe and moderate. The most frequently reported bleeding manifestations were haemarthrosis in severe and moderate patients, and cutaneous bleeding in mild patients. Among 45 postmenarcheal WGH, 21 (46.7%) had history of heavy menstrual bleeding, but only three received treatments. Prenatal diagnosis and management of perinatal haemorrhage were inadequate. Of 34 deliveries in 30 women, nine deliveries were complicated by postpartum haemorrhage, and 22 offspring carried mutations causing haemophilia. Forty-four surgical procedures were performed in 29 patients. Those procedures receiving preoperative coagulation factors coverage were significantly less likely to have excessive bleeding than those who did not (P = .003). CONCLUSION: This is the first and largest study describing WGH in China. There are currently deficiencies in the identification, diagnosis, and management of these patients. Improving health insurance policies, establishing haemophilia centres, and multidisciplinary teams for bleeding and perinatal or perioperative management will help reduce morbidity and mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".