Does an Autoimmune Disorder Following Ovarian Cancer Diagnosis Affect Prognosis?
Bibliographic record
Abstract
We investigated whether developing an autoimmune disorder (AID) following a high-grade epithelial ovarian cancer diagnosis improves overall survival. This retrospective study included data from women treated for high-grade serous, endometrioid, or transitional cell ovarian, fallopian tube, or peritoneal cancer FIGO stage III or IV at a Swiss cantonal gynecological cancer center (2008–2023). We used Kaplan–Meier estimates and the Cox proportional hazards model using time-varying covariates for the survival function estimation. In all, 9 of 128 patients developed an AID following a cancer diagnosis. The median time from cancer diagnosis to AID was 2 years (IQR 2–5). These women survived for a median of 3031 days (IQR 1765–3963) versus 972 days (IQR 568–1819) for those who did not develop an AID (p = 0.001). The median overall survival of nine women with a pre-existing AID was 1093 days (IQR 716–1705), similar to those who never had an AID. The multivariate analyses showed older age (p = 0.003, HR 1.04, 95% CI 1.013–1.064) was associated with a poorer prognosis, and developing an AID after a cancer diagnosis was associated with longer survival (p = 0.033, HR 0.113, 95% CI 0.015–0.837). Clinical manifestations of autoimmune disorders following ovarian cancer diagnoses were associated with better overall survival (8 versus 2.7 years), indicating an overactive immune response may improve cancer control.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".