Survival and prognostic factors in women treated for epithelial ovarian cancer in western region of Saudi Arabia
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
OBJECTIVES: To assess survival and prognostic factors among women with epithelial ovarian cancer in Western Saudi Arabia. METHODS: A retrospective cohort study was carried out between October 2000 and May 2018, reviewing clinical and pathology data of all women who underwent staging or debulking surgery for epithelial ovarian cancer. Analysis of disease-free survival (DFS), overall survivals (OS) and the associated factors used Kaplan-Meier method in addition to cox multivariate regression. RESULTS: A total of 144 patients were included (median age=49.5 years), with a median follow-up time was 3.4 years. Majority (59.7%) of the patients were diagnosed at an advanced stage (III or IV). The mean (95% CI) DFS was 82.3 (67.8-96.8) months, OS was 96.2 (81.3-111.2) months, and the 5-year survival rate was estimated as 38.9%. Univariate analysis showed that older age, clear cell or papillary carcinoma subtypes, serous type, advanced International Federation of Gynecology and Obstetrics (FIGO) stage and the presence of residual disease were associated with poorer DFS and OS (log rank <0.05). Cox regression showed FIGO stage and residual disease >1cm as the strongest prognostic factors independently associated with DFS and OS. CONCLUSION: Improving early diagnosis and achieving optimal cytoreduction are the most critical challenges to achieve significant positive impact on survival of women with epithelial ovarian cancer.
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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 | 0.000 |
| 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 it