Trends, Risk Factors and Treatment of Common Cancers: Outcome of Hospital Based Survey Conducted in Dhaka, Bangladesh
Bibliographic record
Abstract
Background: In Bangladesh, the estimated incidence of 12.7 million new cancer cases will rise to 21.4 million by 2030. Objective: The study aims to provide information about the common cancer types, predisposing risk factors and modalities of treatment among the Bangladeshi cancer patients. Methods: This cross-sectional, descriptive study was conducted between July and December of 2014, in different cancer hospitals located in Dhaka city, Bangladesh. Interviews were conducted based on a semi-structured questionnaire with 550 histopathologically confirmed cancer patients or patients having radio-pathological and clinical evidence of cancer (339 men and 211 women). Results: Among the male patients, the leading cancers were lung (23%), followed by mouth and oropharynx (16.52%), stomach (14.45%), colorectal (10.91%) cancers and others (35.12%). Among the female, breast cancer (31.75%) was the highest, followed by cancer of cervix (27.96%), ovary (18%), mouth and oropharynx (13.27%), stomach (4.74%) and others (4.28%). Among the risk factors of male cancer patients, tobacco smoking was considered highest (76.4%), followed by chewing betel leaf and nuts (61.65%) and chronic disease (58.4%). Among women, the attributable fraction of cancer causing by recurrent STDs (37.91%) was found as the highest, followed by obesity (36.97%), chronic disease (32.7%), chewing betel leaf and nuts (23.22%) and tobacco smoking (26.54%). Main treatment modalities were surgery, chemotherapy and radiotherapy applied either individually or in combination. Most of the patients received chemotherapy (54.72%), followed by chemotherapy with radiation (32%). Only 7.81% patients got palliative care. Conclusion: There are resource-strained oncology units in different public hospitals along with few private hospitals in Dhaka. However, this survey revealed that many patients lack access to cancer awareness programme, cancer screening facilities, availability of low-cost drugs, therapies and palliative care. Hence, promotion of health education, behavioural change communication and development of treatment facilities and manpower are recommended. International Journal of Human and Health Sciences Vol. 09 No. 04 Oct’25 Page: 252-256
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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 | 0.002 |
| 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".