Relationship between diagnosis and intervention in women with inherited bleeding disorders and menorrhagia
Bibliographic record
Abstract
Menorrhagia is the most common bleeding manifestation in women with inherited bleeding disorders. There is little known about whether the management of menorrhagia is altered in specific bleeding disorders. Optimizing treatment strategies for each specific diagnosis may improve quality of life in these women. This work aimed to look for a potential relationship between the specific diagnosis of an inherited bleeding disorder and the intervention required to control the menorrhagia. A retrospective chart review was performed for all women seen in the Kingston Women and Bleeding Disorders Clinic. Patients were categorized by diagnosis into two groups: Haemophilia carriers and all others. Treatment options were grouped into two categories: Medical or gynecological/surgical. Overall, 85.7% of haemophilia carriers required gynaecological surgical management, whereas only 31.4% of patients with all other diagnoses required gynaecological/surgical management (P = 0.012, Fisher's exact test). Therefore, carriers of Haemophilia were more likely to have a better outcome in treating their menorrhagia with gynaecological or surgical management compared with medical management. This information may 1 day help to guide treatment choice for menorrhagia in women with bleeding disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".