Endometrial precancer diagnosis by histopathology, clonal analysis, and computerized morphometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Management of endometrial precancers is compromised by longstanding debate over the natural history of endometrial hyperplasias and inconsistencies in their diagnosis. The recent demonstration that some hyperplasias, like cancers, are phenotypically monoclonal is useful in recognizing biological precancers. A clonal analysis has been undertaken of a series of 93 endometrial tissues and their morphology has been evaluated by subjective diagnostic classification and computerized morphometric analysis. A pathologist's diagnosis of atypical endometrial hyperplasia was highly associated with monoclonal growth. Both microsatellite-stable and microsatellite-unstable precancers were classified as atypical hyperplasias, indicating overlapping morphologies for these two groups. Diagnosis of non-atypical endometrial hyperplasias was not reproducible and identified a group of lesions equally likely to be monoclonal as polyclonal. Computerized morphometry resolved these lesions into monoclonal and polyclonal subgroups with a high degree of accuracy and reproducibility. The predictive value of morphometry was dominated by that fraction of the sample which consisted of stroma (volume percentage stroma). This can be measured manually and used to predict monoclonality when below the threshold value of 55%. This study shows that morphometric analysis reproducibly and precisely identifies monoclonal endometrial precancers from histological sections. It may serve, furthermore, to classify accurately lesions judged by pathologists as indeterminate (non-atypical hyperplasias). The material from this study (available at www.endometrium.org from March 1, 2000) and precisely defined architectural diagnostic criteria provide new tools for diagnostic standardization of endometrial precancers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it