A critical review of recent advances in the diagnosis, classification, and management of uterine adenomyosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to summarize and highlight recent critical advances in the diagnosis, classification, and management of adenomyosis. RECENT FINDINGS: Recent studies have clarified the specific mechanism through which adenomyotic lesions invade the underlying myometrium by epithelial-mesenchymal transition. Correlation studies using diagnostic MRI also strongly support the hypothesis of a different pathogenesis between the inner and outer myometrium forms of adenomyosis. Given advances in diagnostic imaging, several international organizations have also highlighted the importance of classification systems for adenomyosis. Finally, selective progesterone receptor modulators and gonadotropin-releasing hormone antagonists have demonstrated significant promise for treating pelvic pain and bleeding associated with adenomyosis, whereas novel fertility-preserving surgical techniques have been introduced to excise diffuse adenomyotic pathology while maintaining adequate uterine integrity. SUMMARY: Recent attempts at a uniform and reproducible classification system likely represent the first step for the development of a staging system for adenomyosis that can be correlated with the severity of clinical symptoms and promote an individualized therapeutic approach. Simultaneously, further insights into the etiology and pathogenesis as outlined in this review may also help in the development of targeted medical therapies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".