Bibliographic record
Abstract
Background: Epithelial ovarian cancer is the most common cancer of gynaecologic origin in Pakistani women. It ranks amongthe ten most common cancers in our women. Despite being commonly encountered, information regarding the clinicopathological features islacking. Objective: To study the clinical and pathological features of epithelial ovarian cancer in our patients. Study Design: Retrospective study.Setting: Department of Medical Oncology, Jinnah Hospital Lahore. Period: Jan 01,2001 to Dec 31, 2002. Patients and methods: All patientswith histological or cytological diagnosis of epithelial ovarian cancer regardless of stage were included in the study. Information was obtainedfrom medical records which were reviewed thoroughly. Blood samples for analysis of BRCA mutations were sent to University of Toronto,Sunnybrook & Women’s College Health Sciences Centre, Toronto, Canada. Results: 75 patients were accrued. Mean age of the patients was47 years. The well defined risk factors such as nulliparity, lack of lactation, early menarche and late menopause were not present in the majorityof our patients. One striking feature was the number of patients with family history of cancer (18.7%). Majority were first degree relatives of thepatients and most had ovarian or breast cancer. BRCA1 and BRCA2 were seen in nine (12%) of the patients. Clinical presentation and histologicfeatures were similar to American and European patients, the only difference was that a large number (88%) of our patients presented withadvanced (stage III or IV) disease. Conclusions: Epithelial ovarian cancer manifests itself in a younger population of our women. Higherfrequency of positive family history was another striking feature of Pakistani patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".