Different types of tumors in perimenopausal women presenting with ovarian masses at A Tertiary Care Hospital.
Bibliographic record
Abstract
Objectives: To determine frequency of benign and malignant tumors among perimenopausal women presenting with ovarian masses at a tertiary care Hospital. Study Design: Descriptive Cross Sectional study. Setting: Department of Obstetrics & Gynecology, Jinnah Hospital, Lahore. Period: Six Months from August 2017 to January 2018. Material & Methods: A total 127 premenopausal females with ovarian masses visiting Obstetrics & Gynaecology Department, Jinnah Hospital, Lahore were selected. After detailed medical history and clinical examination patients underwent ultrasonography to diagnose status of ovarian masses. Data was entered in self-made proforma. Results: Total 127 patients were selected. Mean age of cases was 48.87 ± 3.04 years, with mean BMI of 26.52±2.43 kg/m2 and obese patients were 30.7%. Out of all 73.2% patients had benign masses and 26.8% patients had malignant masses. Obesity and family history were significantly correlated with malignant tumors among premenopausal women having ovarian masses p-value 0.001. Conclusion: It was observed that the malignant tumors are frequently linked to pre-menopausal women with ovarian masses. Obese and family history positive patients are on high risk of malignant 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 | 0.000 | 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) | 0.002 | 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".