PTEN mutations and evolving concepts in endometrial neoplasia
Bibliographic record
Abstract
Several recent advances have been made in our understanding of the pathogenesis of endometrial tumours, particularly endometrioid endometrial carcinoma (EEC). Mutations in the PTEN gene and microsatellite instability (MSI) are common genetic abnormalities in EECs, and distinguish these lesions from other histological subtypes of endometrial carcinoma. Endometrial precancers are monoclonal lesions that share a common genetic lineage with invasive EEC, including PTEN mutations and MSI. Mutations of the PTEN tumour suppressor gene have been identified in histologically normal-appearing endometrium exposed to oestrogen, 18-55% of endometrial precancers and 26-80% of EECs. PTEN has been shown to play several roles in tumour suppression, including cell cycle arrest and promotion of apoptosis. Loss of PTEN function predisposes endometrial cells to neoplastic transformation, particularly in high-oestrogenic states. MSI is another common alteration seen in EECs and endometrial precancers, and some studies have reported an association between MSI and PTEN mutations. The replication error that results in MSI may facilitate the development of PTEN mutations in some, but not all, cases of EEC. The prognostic significance of PTEN gene mutations and MSI in endometrial carcinoma is controversial. Further study is needed to delineate the different pathogenetic pathways of EEC and their natural history.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".