Bleeding disorders, menorrhagia and iron deficiency: impacts on health‐related quality of life
Bibliographic record
Abstract
von Willebrand disease (VWD) is a bleeding disorder that occurs in up to 1% of the general population. The great majority of females with VWD experience menorrhagia. The morbidity burden in females with VWD may relate to iron deficiency resulting from menorrhagia. To explore relationships between bleeding disorders, menorrhagia, iron deficiency and the outcomes of health-related quality of life (HRQL) and educational attainment. All subjects with VWD, and females with other bleeding disorders, in the Canadian national registry who were more than 12 years of age were eligible for survey. Survey measures included the HEALTH UTILITIES INDEX(®); abridged Clinical History Assessment Tool; socio-demographic questions and serum ferritin. Statistical analyses included testing differences among groups of means using analysis of variance and of proportions using chi-squared test. Significant size differences in mean HRQL scores were detected between VWD females and both females with other bleeding disorders [diff = (-0.08); P = 0.017] and VWD males [diff = (-0.07); P = 0.039]. Mean HRQL scores differed between females with and without menorrhagia (P < 0.001). Mean HRQL scores were not significantly different between females with and without iron deficiency. Educational attainment was not associated with disease group, menorrhagia status or iron status. Females with VWD have a greater morbidity burden than females in the general population, females with other bleeding disorders and males with VWD. Menorrhagia is associated with low HRQL scores in females with bleeding disorders, including VWD. Further investigation should assess how menorrhagia impacts HRQL in females with bleeding disorders.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".