Breast cancer services in Vietnam: a scoping review
Bibliographic record
Abstract
BACKGROUND: Breast cancer incidence has been increasing consistently in Vietnam. Thus far, there have been no analytical reviews of research produced within this area. OBJECTIVES: We sought to analyse the nature andextent of empirical studies about breast cancer in Vietnam, identifying areas for future research and systemsstrengthening. METHODS: We undertook a scoping study using a five-stage framework to review published and grey literature in English and Vietnamese on breast cancer detection, diagnosis and treatment. We focused specifically on research discussing the health system and service provision. RESULTS: Our results show that breast cancer screening is limited, with no permanent or integrated national screening activities. There is a lack of information on screening processes and on the integration of screening services with other areas of the health system. Treatment is largely centralised, and across all services there is a lack of evaluation and data collection that would be informative for recommendations seeking to improve accessibility and quality of breast cancer services. CONCLUSIONS: This paper is the first scoping review of breast cancer services in Vietnam. It outlines areas for future focus for policy makers and researchers with the objective of strengthening service provision to women with breast cancer across the country while also providing a methodological example for how to conduct a collaborative scoping review.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".