Abstract B033: A Comparative Study of Cervical Cancer Treatment Outcomes Between Reproductive-Aged and Post-Menopausal Women in a Tertiary Hospital in Bangladesh.
Bibliographic record
Abstract
Abstract Background: Cervical cancer is the second most prevalent cancer among females, following breast cancer, accounting for 13.3% of cases, whereas breast cancer accounts for 18% (WHO, 2022, version 11). The mortality rate of cervical cancer is around 5%, with post-menopausal patients being the most frequently affected group. Older patients tend to present at more advanced stages of the disease, leading to poorer treatment outcomes compared to those in the reproductive age group. This study aims to compare the treatment outcomes between reproductive-aged women and post-menopausal women. Methodology: Data were collected from Chittagong Health Point Hospital Ltd in Bangladesh, spanning from September 2019 to May 2023, focusing on cervical cancer treatment outcomes, including hysterectomy followed by chemotherapy and radiotherapy, with follow-ups at 3- and 6-months post-treatment. Results: A total of 35 patient records were analyzed. Of these, 15 (42.85%) were under 45 years old (reproductive age group), and 20 (57.15%) were over 50 years old (post-menopausal group). Most younger patients (73.33%) were diagnosed at an early stage (1A-1B1), while 4 out of 15 (26.66%) were diagnosed at a locally advanced stage (1B2-4A). After treatment, 93.33% of the younger patients showed positive prognoses. At the 3-month follow-up, two patients (13.33%) were found to have stage 1 cancer and underwent further chemotherapy and radiotherapy, achieving remission by the 6-month follow-up. One patient (6.67%) passed away during treatment. Among post-menopausal women, 65% (13 out of 20) were diagnosed at a locally advanced stage, with only 7 patients (53.85%) fully recovering by the 6-month follow-up. Four patients (20%) presented with advanced stage (4B) cancer and showed poor outcomes. Only three patients (15%) were diagnosed at an early stage, with relatively better prognoses. Notably, 35% (7 out of 20) refused to continue treatment, and 3 patients (15%) died during their treatment. Conclusion: The data indicate that younger patients are more likely to be diagnosed early, resulting in significantly better prognoses compared to their older counterparts. Citation Format: Farzana Sultana. A Comparative Study of Cervical Cancer Treatment Outcomes Between Reproductive-Aged and Post-Menopausal Women in a Tertiary Hospital in Bangladesh. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B033.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".