Bibliographic record
Abstract
Endometrial stromal tumors are rare uterine mesenchymal neoplasms that have intrigued pathologists for years, not only because they commonly pose diagnostic dilemmas, but also because the classification and pathogenesis of these tumors has been widely debated. The current World Health Organization recognizes 4 categories of endometrial stromal tumor: endometrial stromal nodule (ESN), low-grade endometrial stromal sarcoma (LG-ESS), high-grade endometrial stromal sarcoma (HG-ESS), and undifferentiated uterine sarcoma (UUS). uterine sarcoma. These categories are defined by the presence of distinct translocations as well as tumor morphology and prognosis. Specifically, the JAZF1-SUZ12 (formerly JAZF1-JJAZ1) fusion identifies a large proportion of ESN and LG-ESSs, whereas the YWHAE-FAM22 translocation identifies HG-ESSs. The latter tumors appear to have a prognosis intermediate between LG-ESS and UUS, which exhibits no specific translocation pattern. This review (1) presents the clinicopathologic features of endometrial stromal tumors; (2) discusses their immunophenotype; and (3) highlights the recent advances in molecular genetics which explain their pathogenesis and lend support for a new classification system.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".