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Record W4396976030 · doi:10.1080/17441692.2024.2351186

Cancer screening research in Bangladesh: Insights from a scoping review

2024· review· en· W4396976030 on OpenAlexaff
Nazirum Mubin, Tasmira Mohib, Nashit Chowdhury, Taharat Fatema Chowdhury, Ahmad Maksud Hasan Laskar, Sanchita Sultana, Mohammad Zahir Raihan, Tanvir Chowdhury Turin

Bibliographic record

VenueGlobal Public Health · 2024
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCancer screeningMedicineBreast cancer screeningCervical cancerFamily medicineCancerBreast cancerEnvironmental healthMammography

Abstract

fetched live from OpenAlex

This scoping review summarises the findings of research conducted on cancer screening in Bangladesh, including the prevalence, awareness, barriers, and evaluation of screening programmes, by performing a comprehensive search of electronic databases and gray literature. 25 studies that met inclusion criteria were included in the final analysis. Most of the studies were about screening for cervical cancer, were quantitative, were cross-sectional, and were conducted in hospital settings. The main challenges to screening uptake were shyness, fear, a lack of knowledge, and an inadequate understanding of the concept of screening. Visual inspection with acetic acid (VIA) was found to be a simple and cost-efficient way to detect early-stage cervical cancer. However, breast self-examination (BSE) was reported to be insufficient. Education was found to have a positive impact on cancer screening knowledge and practice, but more needs to be done to improve screening rates, such as the utilisation of media, particularly in rural areas. The results of this scoping review highlight Bangladesh's low cancer screening prevalence and uptake and suggest that targeted awareness campaigns and enhanced access to screening services are required to increase cancer screening uptake and reduce the cancer burden in Bangladesh.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.581
GPT teacher head0.583
Teacher spread0.002 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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