Pattern of Uterine and Cervical Lesions Marked on Histopathological Examination of Hysterectomy Specimens
Bibliographic record
Abstract
Background: In Gynecology, most of the knowledge circles around the female reproductive tract and uterus has been a vital organ playing central role in female reproductive life cycle both in terms of menstrual and ostereous cycles. Uterus is subjected to both mechanical and hormonal stresses so uterine pathologies and diseases are the most common pathologies marked in female reproductive systems. These pathologies range from benign uterine pathology to malignancy in many cases. Most of the uterine and cervical pathologies are diagnosed after hysterectomy on histopathological examination only. Objective: The study was carried out to see histopathological patterns of uterine and cervical lesions in hysterectomy specimens. Study Design: Histopathology department of Women Medical and Dental College, Abbottabad from August 2021 to August 2022. Materials and Methods: It was a retrospective study done on hysterectomy samples findings in histopathological examination of uterine and cervical area on 80 specimens over a 1 year period. SPSS latest version was used for DATA. Results: 80 patients were included based on histopathological reports of the post hysterectomy specimens. Age of patients lies < 30 years to > 60 years. Majority of the cases were between 41-50 years and 51-60 years of the age while patients between 41-50 years age group were 50 %,adenomyosis (09%), atrophied uterus (5%), Hyperplasia (10%), leiomyoma with adenomyosis (18.5%), Leiomyoma (55%) and leimyosarcoma (2.5%),while cervical pathologies show cervicitis, (62%), cervical dysplasia (21%), cervicitis (papillary) in 07 %, and 10% cases were normal. Different uterine lesions based on the age of the patients show that Leiomyoma is maximally found in patients between 46-60 years and above 60 years of patients and only two cases were marked below 30 Years. Conclusion: The commonest benign lesion was leiomyoma in uterus followed by adenomyosis combined with leiomyoma, while in cervical canal commonest finding was cervicitis in Hysterectomy specimen. Keywords: Adenomyosis, Histopathology, Hysterectomy, Leiomyoma, Cervicitis.
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.
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 | 0.002 | 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) | 0.003 | 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".