Predictors of survival rates among breast cancer patients in Ethiopia: a systematic review and meta-analysis 2024
Bibliographic record
Abstract
INTRODUCTION: Breast cancer remains the most common cancer and a leading cause of cancer-related deaths among women worldwide. In Ethiopia, the survival rate of breast cancer patients is influenced by various socio-demographic, clinical, and health system factors. This systematic review and meta-analysis aimed to identify and synthesize the predictors of survival rates among breast cancer patients in Ethiopia. METHODS: We conducted a systematic review of observational cohort studies. The literature search was performed between August 1 and 30, 2024, using PubMed, Hinari, EMBASE, Google, Google Scholar, and Web of Science. 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: A total of 15 articles with 6,375 study participants from six regions were included. We found that significant predictors of decreased survival rate among breast cancer patients were age (aHR 1.05, 95% CI 1.02-1.08), illiteracy (aHR 7.34, 95% CI 4.38-10.3), married (aHR 1.21, 95% CI 1.03-1.40), rural residence (aHR 1.71, 95% CI 1.06-2.36), two or more lymph node involvement (aHR 3.57, 95% CI 1.02-6.13), histological grade two or more (aHR 1.44, 95% CI 1.12-2.77), overweight (aHR 0.56, 95% CI 0.24-0.87), and having comorbidity (aHR 1.86, 95% CI 1.04-2.68). CONCLUSION: This systematic review and meta-analysis identified several key predictors of reduced survival rates among breast cancer patients in Ethiopia, including older age, illiteracy, rural residence, involvement of two or more lymph nodes, higher histological grade, marital status, and the presence of comorbidities. Interestingly, being overweight was associated with improved survival. Health stakeholders and policymakers emphasizing public health education, managing comorbidities, and expanding access to early detection and treatment, especially in rural areas, are critical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".