Different types of tumors in perimenopausal women presenting with ovarian masses at A Tertiary Care Hospital.
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 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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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