A Study on Causes for Hysterectomy in Bangladesh
Bibliographic record
Abstract
Background: Hysterectomy is one of the most common gynecological surgeries performed worldwide, including in Bangladesh, where concerns are rising over the frequency and justification of the procedure, particularly among younger women. Objective: The aim of this study was to assess the causes leading to hysterectomy and evaluate the demographic and clinical profiles of women undergoing the procedure in a clinic-based setting in Bangladesh. Method: This randomized prospective study was conducted at Khalishpur Clinic from January 2018 to April 2024. A total of 366 women who underwent hysterectomy during this period were included. Data were collected using a structured proforma documenting patient demographics, reproductive history, and clinical diagnoses. Descriptive statistics were used to analyze the data and identify common patterns and causes. Results: The mean age of the participants was 45.7 ± 11.7 years, with 75% having two to four children, indicating completion of childbearing. Adenomyosis was the most prevalent cause of hysterectomy (79.5%), followed by fibroids (41.8%), and chronic cervicitis (21.3%). Other causes included uterine prolapse (16.1%), endometriosis (3.0%), and endometrial hyperplasia (1.9%). Notably, 50% of women had two co-existing gynecological conditions, while 16.7% presented with three to five, underscoring the complexity of clinical presentations. Conclusion: The study reveals that hysterectomy in Bangladesh is predominantly performed in women in their mid-40s with completed childbearing.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".