Ovarian carcinosarcoma is a distinct form of ovarian cancer with poorer survival compared to tubo-ovarian high grade serous carcinoma
Bibliographic record
Abstract
ABSTRACT BACKGROUND Ovarian carcinosarcoma (OCS) is an uncommon, biphasic and highly aggressive ovarian cancer type, which has received relatively little research attention. METHODS We curated the largest pathologically-confirmed OCS cohort to date, performing detailed histopathological characterisation, analysis of features associated with survival, and comparison against high grade serous ovarian carcinoma (HGSOC). RESULTS 82 OCS patients were identified; overall survival was poor (median 12.7 months). 79% demonstrated epithelial components of high grade serous (HGS) type, while 21% were endometrioid. Heterologous elements were common (chondrosarcoma in 32%, rhabdomyosarcoma in 21%, liposarcoma in 2%); chondrosarcoma was more frequent in OCS with carcinomatous components of endometrioid type. Earlier stage, complete surgical resection, and treatment with platinum-containing chemotherapy were associated with prolonged survival; however, risk of relapse and mortality was high across all patient groups. Histological subclassification did not identify subgroups with distinct survival. Compared to HGSOC, OCS patients were older at diagnosis (P<0.0001), more likely to be FIGO stage I (P=0.025), demonstrated lower chemotherapy response rate (P=0.001) and had significantly poorer survival (P<0.0001). CONCLUSION OCS represents a distinct, highly lethal form of ovarian cancer for which new treatment strategies are urgently needed. Aggressive adjuvant chemotherapy should be considered for all patients, including those with early stage disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".