Management of the Elderly Patients with High-Grade Serous Ovarian Cancer in the REAL-WORLD Setting
Bibliographic record
Abstract
Treatment of elderly patients with neoplasia is challenging. Age is a known prognostic factor in ovarian cancer but the optimal treatment of elderly patients has not been determined. We undertook a retrospective analysis to determine clinical practice in advanced-stage ovarian cancer patients older than 70 years of age. Methods: Medical records of women with high-grade serous ovarian cancer, stage III and IV were retrospectively analyzed. Results: A total of 735 patients were identified with a median age of 61.5 years. 22.4% among them were older than 70 years of age at diagnosis. First-line Progression-Free Survival (PFS) and Overall Survival (OS) were significantly worse in elderly patients in comparison to the younger ones [mPFS 11.3 months vs. 14.8 months, (p < 0.001) and mOS 30.2 months vs. 45.6 months (p < 0.001)]. However, elderly patients were characterized by worse ECOG-Performance Status and they were more frequently treated with Neoadjuvant Chemotherapy followed by Interval Debulking Surgery, while often they were more frequently denied debulking surgery compared to patients under 70 years of age. Moreover, elderly patients received more frequently monotherapy with platinum as frontline treatment. In contrast, there was no significant difference in the outcome of the debulking surgery in comparison to the younger patients or the frequency that gBRCA test was performed. Age over 70 years did not retain its significance for either Progression-Free Survival or Overall Survival when adjusted for all other reported prognostic factors. Conclusions: Elderly ovarian cancer patients have a worse prognosis. Comprehensive geriatric assessment should be performed for the optimal treatment of these patients.
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.
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) | 0.001 | 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".