Mortality rate and predictors of colorectal cancer patients in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction The incidence of colorectal cancer (CRC) has been increasing in Sub-Saharan countries, including Ethiopia. However, the real mortality rate for CRC patients in Ethiopia has not been established. Therefore, this systematic review and meta-analysis aimed to determine the overall mortality rate and identify predictors among CRC patients in Ethiopia. Methods PubMed, EMBASE, Web of Science, Scopus, Science Direct, and Google Scholar were searched to identify relevant articles. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) were followed. The quality of the included studies was assessed using the Newcastle-Ottawa Scale Critical Appraisal checklist. A random effect model was used to estimate the pooled mortality rate and adjusted hazard ratio (AHR). Publication bias was assessed using funnel plots and Egger’s regression test, while heterogeneity was evaluated through the Cochran Q test and I2statistics. Results After reviewing 74 articles, only 7 studies met the criteria and were included in the analysis. The analysis revealed that the overall mortality rate among CRC patients in Ethiopia was 40.5% (95% confidence interval [CI]: 32.05, 48.87) while the survival rates at 1 year, 3 years, and 5 years were 82.3% (95% CI: 73.33, 91.31), 48.8% (95% CI: 43.35, 54.32), and 26.6% (95% CI: 21.26, 31.91) respectively. Subgroup analysis indicated that studies conducted after 2017 had higher mortality rates compared to those studied earlier (43.0% vs. 38.2%). Older age (AHR: 1.89, 95% CI: 1.27, 2.82); being married (AHR: 2.53, 95% CI: 1.79, 3.57); having comorbidities (AHR: 1.84, 95% CI: 1.45, 2.35); having high CEA levels (AHR: 2.06, CI: 1.35, 3.13); being in stage II (AHR: 4.13, 95% CI: 1.85, 9.22), III (AHR: 8.62, 95% CI: 3.88, 19.15), and IV (AHR: 8.06, CI: 2.89, 22.49) were the most important predictors. Conclusion In Ethiopia, the mortality rate among individuals diagnosed with CRC is high, with two out of five patients dying from this disease. Age, marital status, CEA level, comorbidities, and cancer stage were identified as predictors of mortality in CRC patients. Therefore, early detection and screening should be prioritized, particularly for older patients, those who are married, have comorbidities, elevated CEA levels, and advanced cancer stages.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".