Intravenous Estrogen for Acute Heavy Menstrual Bleeding: Commentary and Database Analysis
Bibliographic record
Abstract
Background: Heavy menstrual bleeding (HMB) is common. Although hormonal medication is the mainstay of treatment, there is no scientific evidence to support the superiority of one regimen over another. Both oral and intravenous (IV) forms of estrogen are used for HMB in the acute setting and are known to be associated with thrombosis, in particular, venous thromboembolism (VTE) in the deep veins of the legs or pulmonary vessels. Progestins are also used for treatment of HMB; a common choice is norethindrone acetate, a small amount of which is metabolically converted to ethinyl estradiol (an estrogen) after ingestion. We sought to assess the incidence and relative risk (RR) of VTE for users of IV estrogen, oral ethinyl estradiol, and norethindrone acetate. Methods and Results: A retrospective descriptive review of a large de-identified database (TriNETX, LLC) revealed a significantly higher calculated rate and RR of VTE amongst users of injectable conjugated estrogen compared to users of oral ethinyl estradiol and oral norethindrone acetate. Conclusion: Research is needed to determine the true RR of thrombosis for IV estrogen users to allow clinicians and patients to make informed decisions that appropriately stratify risks and benefits when considering the options for hormonal treatment of acute HMB.
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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 | 0.027 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".