Bibliographic record
Abstract
1. To become familiar with the entity of uterine PEComa, along with the ultrasound and correlative MRI appearances of uterine PEComa. 2. To understand the importance of histopathology and immunohistochemistry in the diagnosis of PEComa. 3. To learn about the management of uterine PEComa. 1. Presentation of a series of pathology-proven uterine PEComas, with a focus on initial ultrasound imaging appearances and correlative appearance on MRI. 2. Review the distinctive histopathology and immunohistochemistry of PEComas. 3. Discussion on the management of uterine PEComas. A series of 5 pathology-proven uterine PEComas were reviewed, with patient age ranging from 29 to 80 years old. Uterine PEComas on ultrasound most often presented as a large solid uterine mass ranging in size from 2.5 to 13.0 cm and were mistakenly diagnosed as uterine fibroids (80%). One mass was misdiagnosed as a mesenteric GIST. Pathology showed malignant features of increased mitotic activity, necrosis, and marked atypia in 60% of cases. On MRI, all masses demonstrated intermediate T2-signal intensity, 40% hemorrhage, and 60% cystic/necrotic change. Uterine PEComas often present as a large uterine mass on ultrasound and can have a varied appearance mimicking that of uterine fibroids or uterine sarcomas. A prospective diagnosis of PEComa can be included when a growing uterine mass is identified, with atypical features of a fibroid and corresponding intermediate T2-signal intensity is seen on MRI (i.e. T2 signal higher than that of a typical fibroid or skeletal muscle). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".