Menstruation, anticoagulation, and contraception: VTE and uterine bleeding
Bibliographic record
Abstract
Abnormal or excessive menstrual bleeding affects one-third of reproductive-aged women. This number increases to 70% among women on direct oral anticoagulants (DOACs). While there is some variation in frequency of heavy menstrual bleeding (HMB) with different DOAC options, all menstruating individuals should receive counseling about the risk of HMB at the time of DOAC initiation. Management options include progestin-only therapies such as the levonorgestrel intrauterine system and etonogestrel subdermal implant or the progestin-only pill. Combined hormonal contraceptives and depot medroxyprogesterone acetate are associated with increased rates of thrombosis in nonanticoagulated women but may be continued, or even initiated, so long as therapeutic anticoagulation is ongoing. Procedural therapies, such as endometrial ablation, uterine artery embolization, or hysterectomy, are considerations for women who have completed childbearing and for whom more conservative measures are objectionable or ineffective. Given the high rates of HMB in women on DOACs, management strategies should be discussed even before heavy bleeding is diagnosed, particularly in women who experienced HMB prior to DOAC initiation. As iron deficiency with or without anemia is a common complication of HMB, complete blood count and ferritin levels should be monitored periodically, and iron deficiency should be treated with oral or intravenous iron supplementation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".