How I treat heavy menstrual bleeding associated with anticoagulants
Bibliographic record
Abstract
Anticoagulant-associated heavy menstrual bleeding (HMB) is an underrecognized but not uncommon problem in clinical practice. Premenopausal women should be advised of the potential effect of anticoagulant therapy on menstrual bleeding at the time of treatment initiation. Consequences of HMB should be assessed and treated on an ongoing basis. In the acute setting, the decision to withhold anticoagulants is based on an individual patient's risk of thrombosis and the severity of the bleeding. For women who require long-term anticoagulation, a levonorgestrel intrauterine system, tranexamic acid (during menstrual flow), high-dose progestin-only therapy, or combined hormonal contraceptives are effective for controlling HMB. The risk of thrombosis during anticoagulant therapy with these treatments is not well studied but is likely to be low. Selection of type of hormonal therapy is based on patient preference, other indications for and contraindications to therapy, adverse effect profile, and ongoing thrombotic risk factors. Women who do not respond to medical treatment or who do not wish to retain their fertility should be considered for surgical management.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".