Metastatic ovarian carcinoma to the brain: An approach to identification and classification for neuropathologists
Bibliographic record
Abstract
Brain metastasis is an uncommon but increasing manifestation of ovarian epithelial carcinoma and neuropathologists' collective experience with these tumors is limited. We present clinicopathological characteristics of 13 cases of brain metastases from ovarian epithelial carcinoma diagnosed at two academic institutions. The mean ages at diagnosis of the ovarian carcinoma and their subsequent brain metastases were 58.7 and 62.8 years, respectively. At the time of initial diagnosis of ovarian carcinoma the majority of patients had an advanced stage and none had brain metastases as their first manifestation of malignancy. Brain metastases tended to be multiple with ring-enhancing features on neuroimaging. Primary tumors and their brain metastases were all high-grade histologically and the histologic subtypes were: nine high-grade serous carcinoma (HGSC) cases, two clear cell carcinoma (CCC) cases and a single case each of carcinosarcoma and high-grade adenocarcinoma. A recommended histo- and immunopathological approach to these tumours are provided to aid neuropathologists in the recognition and classification of metastatic ovarian carcinoma to the brain.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".