GENDER DIFFERENCES IN BLEEDING PROBLEMS AND IMPLICATIONS FOR THE ASSESSMENT OF A BLEEDING DISORDER
Bibliographic record
Abstract
Background: The value of gender-specific questions in assessing patients for bleeding problems is not well established, nor is the impact of bleeding on sexual and reproductive health. Methods: We administered the CHAT (clinical history assessment tool) to 256 females and 66 males referred for bleeding problems, and 67 female and 32 male healthy controls. This detailed bleeding history questionnaire included questions about sexual health, blood in semen, and bleeding with circumcision, menses, pregnancies, and childbirth. A diagnosis for each patient was established by independent medical record review. The prevalence of symptoms among patients and controls was compared. Results: 80% of patients had bleeding disorders (205 women, 54 men). Men with bleeding disorders did not have significantly increased intercourse-related bleeding, bleeding affecting their sex life, or gender-specific bleeding. Women with bleeding disorders had significantly increased intercourse-related bleeding and bleeding affecting their sex life. They also had significantly increased risks of prolonged menses, menses interfering with lifestyle, and menses requiring medical or surgical therapy (all p values < 0.001). Women with bleeding disorders were more concerned about pregnancy and delivery because of bleeding (p=0.0001), and 38% had been told by a doctor not to become pregnant. Yet they did not have increased risks for pregnancy loss or bleeding during pregnancy (p values > 0.1) and had similar numbers of offspring as controls (mean 2.0 vs.1.7). Conclusion: Gender has an important impact on the manifestations, diagnosis and management of common bleeding disorders. Detailed questions about gender-specific bleeding are useful in assessing women with possible bleeding disorders, as they are at greater risk for bleeding that negatively impacts their sexual and reproductive health.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".