Etiology and diagnosis of heavy menstrual bleeding among adolescent and adult patients: a systematic review and meta-analysis of the literature
Bibliographic record
Abstract
BACKGROUND: Heavy menstrual bleeding (HMB) is excessive menstrual blood loss that interferes with an individual's quality of life. Many individuals with HMB are inadequately managed by health care providers. OBJECTIVES: This systematic review aims to provide a comprehensive summary of the etiologies and diagnosis of HMB while calculating the prevalence of underlying causes among premenopausal patients and quantifying the test accuracy of diagnostic strategies. METHODS: MEDLINE, EMBASE, the Cochrane Library, and Web of Science were searched since inception to include studies investigating the prevalence of underlying etiology and diagnostic accuracy of investigations for HMB. The primary outcome was the prevalence of the causes of HMB, secondary outcome included the prevalence of etiology by age. Meta-analyses were conducted via random-effects model. RESULTS: In total, 53 studies were included. Forty-five studies included data on the prevalence of underlying HMB etiology, totaling 41 541 patients. The overall prevalence of bleeding disorders was 30% (95% CI, 14-46); von Willebrand disease, 8% (95% CI, 7-10); platelet function defect, 9% (95% CI, 7-12); abnormal thyroid, 3% (95% CI, 0-6); and polycystic ovarian syndrome, 8% (95% CI, 4-12). Subgroup analysis showed bleeding disorders were prevalent in 16% (95% CI, -8 to 41) of adults with HMB but in 39% (95% CI 18-60) of adolescents with HMB. CONCLUSION: Many diagnoses were associated with bleeding disorders and, therefore, warrant investigation when assessing a patient with HMB of unknown etiology. The causes are likely age dependent and should be considered when diagnosing 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".