Proportion of early-stage breast cancer at diagnosis in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common cancer-affecting women globally, with disproportionally high mortality rates in lower-income countries, including Ethiopia. The stage at diagnosis is a well-defined predictive system that determines the likelihood of breast cancer mortality. Early-stage breast cancer at diagnosis is associated with better treatment outcomes as compared with late stage. Although there are numerous primary studies on women with breast cancer with different proportions of early-stage breast cancer, there is currently no summary data on what proportion of breast cancer was diagnosed at early-stage in Ethiopia. This study focused on a pooled proportion of early-stage breast cancer at diagnosis in Ethiopia. METHODS: By using key terms, Medline through Pub-Med, Google Scholar, Science Direct, HINARI and Medley were searched about breast cancer in Ethiopia, and a total of 288 articles were retrieved. After screening the articles and quality of each article was assessed using Newcastle-Ottawa Scale. Finally, 41 articles were used for the final pooled proportion. A random effects model was used to estimate the pooled prevalence and heterogeneity of included studies that were then assessed by using prediction interval. RESULTS: Pooled proportion of early-stage breast cancer at diagnosis in Ethiopian hospitals was found to be 36% with a 95% confidence interval ranging from 31 to 41% and a 95% prediction interval ranging from 28 to 45%. CONCLUSION: Most breast cancer patients (64%) in Ethiopia are diagnosed at a late-stage. This contributes to the high mortality rates of breast cancer among women in Ethiopia. Therefore, in line with recommendations by the World Health Organization, we recommend that there should be an emphasis in Ethiopia to focus on early detection of breast cancer.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".