The history of women and hemophilia: a narrative review of evolving beliefs and testing practices
Bibliographic record
Abstract
The history of hemophilia is well documented, yet reports focus heavily on the male perspective and severe forms of the disease. Although hemophilia was initially believed to only affect men with women seen as silent carriers, it is now universally acknowledged that women and girls can also be affected. In this narrative review, we tracked the progression of beliefs about women and hemophilia as documented in the literature from pre-1800s to the present time. We present a timeline of evolving beliefs and testing practices and identified 9 distinct time periods when key shifts occurred related to various scientific discoveries. Our review highlights how women affected by hemophilia experienced complete dismissal of their health issues despite evidence of bleeding symptoms as early as the 1900s. We identified 1990 as a major timepoint for shifting beliefs when large scale acknowledgment that hemophilia also affects women is documented and systematic testing for bleeding risk is first suggested. Women evolve from being seen as unaffected genetic transmitters only, to being recognized as a population affected by hemophilia in unique ways requiring timely testing and effective treatment. Yet, despite this clear progress, recent publications continue to document many persistent issues such as delayed diagnosis, untreated symptoms, and barriers to care. Ongoing research and advocacy efforts are required to improve knowledge translation until real-world outcomes are seen in screening, diagnosis, treatment, and prevention of bleeding.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".