How should abnormal uterine bleeding be managed in people with bleeding disorders: a systematic review of the literature and thematic synthesis
Bibliographic record
Abstract
Abnormal uterine bleeding (AUB) describes any bleeding from the uterus that deviates from the norm in terms of regularity, duration, or volume. AUB is a common condition that can significantly affect quality of life. Although inherited bleeding disorders (IBDs) can cause heavy menstrual bleeding, there is no clear consensus on how AUB is best managed in those patients. This study aimed to address this knowledge gap using evidence based on clinical findings to define the best management of AUB in patients with IBD by conducting a systematic review of the literature. Searches were conducted for articles published from January 1, 2000, until May 6, 2024 in the Embase (PubMed), Medline, Scopus, Cochrane library, Google Scholar, and Cumulative Index to Nursing and Allied Health Literature complete via the Elton B. Stephens Company databases. In total, 244 studies were assessed for eligibility based on inclusion and exclusion criteria. Included studies were appraised for risk of bias and quality assurance using the Newcastle Ottawa Scale, after which data was systematically coded to generate descriptive and analytical themes. Thirteen studies were included in the thematic synthesis, encompassing over 893 participants. Thematic synthesis identified hormonal treatments, such as the levonorgestrel-releasing intrauterine system (LNG-IUS), to be largely effective in the symptom management of AUB in IBDs. Treatment of AUB patients with LNG-IUS, followed by tranexamic acid or 1-deamino-8-d-arginine vasopressin (DDAVP) commonly led to amenorrhea. The use of LNG-IUS as first-line therapy is recommended for those with AUB, followed by the use of combination therapy such as tranexamic acid and desmopressin. We identified the need to strengthen communication between specialists involved in the care of those with AUB and IBDs.
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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 | 0.059 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".