Delayed presentation of breast cancer patients and contributing factors in East Africa: Systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Breast cancer remains a significant public health issue, with delayed medical attention often leading to advanced stages and poorer survival rates. In East Africa, evidence on the prevalence and factors contributing to the delayed presentation of breast cancer is limited. As a result, this study aims to assess the pooled prevalence of delayed breast cancer presentation and identify contributing factors in East Africa. METHODS: We conducted a systematic review of observational studies from East Africa using PubMed, Google Scholar, Embase, Cochrane Library, Hinari, and Mednar databases. The Newcastle Ottawa 2016 Critical Appraisal Checklist assessed methodological quality. Publication bias was evaluated using a funnel plot and Egger's test, and heterogeneity was examined with the I-squared test. Data were extracted with Microsoft Excel and analyzed using Stata 11. RESULTS: The pooled prevalence of delayed presentation among breast cancer patients in East Africa was 61.85% (95% Confidence Interval: 48.83%-74.88%). Significant factors contributing to delayed presentation included visiting traditional healers (Adjusted Odds Ratio: 3.52; 95% CI: 1.43-5.59), low educational levels (Adjusted Odds Ratio: 3.61; 95% CI: 2.39-4.82), age>40 years (Adjusted Odds Ratio 1.87; 1.03, 2.71), absence of breast pain (Adjusted Odds Ratio 2.42; 1.09, 3.74), distance >5km away from home to health institution (Adjusted Odds Ratio 2.89; 1.54, 4.24), and rural residence (Adjusted Odds Ratio: 3.33; 95% CI: 2.16-4.49). CONCLUSION: This meta-analysis's findings demonstrated that over half of breast cancer patients in East Africa delayed detection. Significant factors associated with delayed presentation include age over 40 years, illiteracy, rural residence, use of traditional healers, distance greater than 5 km from a health facility, and absence of breast pain. Healthcare stakeholders and policymakers must be focused on raising awareness and educating people to encourage early detection and prompt therapy.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".