Frequency and Type of Ovarian Tumors in Gynecological Women presenting to DHQ Teaching Hospital Gujranwala
Bibliographic record
Abstract
Background: Ovarian cancer (OC) average lifetime risk is 1 in 70(1.4%).Ovarian cancer is diagnosed in 7300 women every year in UK and 239,000 women world wide.There are variations in incidence with ethnicity, white women have the highest incidence approximately14/100,000 where as Asian women have a lower incidence at 10/100,000. Aim: To find the frequency and different types of ovarian carcinomas in women presenting with pain lower abdomen. Methods: This cross-sectional study was conducted in the Department of Obstetrics and Gynecology, DHQ Hospital Gujranwala from1st May 2020 to 31st October 2020.A total of 282 women presenting with pain lower abdomen were included.All women underwent ultrasonography and ovarian tumors were noted as per operational definition. Patients with ovarian tumor underwent laparotomy, the specimen of tumors were collected with excision biopsies and sent for histopathological analysis. Results: The mean age of cases was 33.808±7.70 years, and the mean duration of complaint was 4.322±1.46 weeks and mean weight was 69.560±13.00 kg. Majority of the patients (82.3%) belonged to 20-40 years age groups. Ovarian Tumor was seen in 65(23%) patients. Among 65 patients with ovarian tumor, 72.3% were benign, 3.1% borderline and 24.6% were malignant. Conclusion: It was concluded that ovarian tumors were common between the 20 and 40 years of age. The frequency of Malignant neoplastic lesions was higher than the benign neoplastic lesions. Keywords: Women, Pain lower abdomen, Ovarian tumors
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".