Diagnosis and medical management of abnormal premenopausal and postmenopausal bleeding
Bibliographic record
Abstract
Abnormal uterine bleeding is a common reason for presentation to health-care providers: it is estimated that one woman in three will present to a care provider with abnormal uterine bleeding (AUB) during the reproductive years, and that at least one woman in 10 will experience postmenopausal bleeding. Although there are some variations in national guidelines for investigation, diagnosis and management of premenopausal AUB, there are far more areas of agreement than disagreement. A comprehensive literature search was undertaken to review national and international guidelines regarding investigation, diagnosis and management of AUB in both premenopausal and postmenopausal women. Areas of controversy are identified, and latest evidence reviewed. Although efforts to reduce hysterectomies for premenopausal AUB through medical management have largely been successful, there are areas where more research is necessary to guide optimal investigation and management. Many countries have well-defined guidelines for investigation and management of premenopausal AUB: there are fewer well-developed guidelines for investigation and management of postmenopausal bleeding. There is a paucity of evidence-based data on management of unscheduled bleeding on menopausal hormone therapy.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".