Survival Rate of Colorectal Cancer in Eastern Mediterranean Region Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Colorectal cancer (CRC) is the second most common cause of cancer-related deaths worldwide. Survival rates are among the most important factors in quality control and assessment of treatment protocols. This study was aimed to assess the survival rate of colorectal cancer in Eastern Mediterranean Region Countries. In the present study we comprehensively searched 6 international databases including PubMed/Medline, ProQuest, Scopus, Embase, Web of Knowledge and Google Scholar for published articles until November 2018. The Newcastle-Ottawa Quality Assessment Form for Cohort Studies was applied to evaluate the quality of included studies. The heterogeneity of papers was assessed with the Cochran Test and I-Square statistics. Meta-regression test was performed based on publication year, sample size and Human Development Index (HDI) of each study. Among the total of 1023 titles found in the systematic search, 43 studies were eligible to be included in the present meta-analysis. According to the results, the 1-year, 3-year and 5-year survival rate of patients with Colorectal Cancer was 88.07% (95% CI, 83.22-92.92), 70.67% (95% CI, 66.40-74.93) and, 57.26% (95% CI, 50.43-64.10); respectively. Furthermore, Meta-regressions did not show significant correlations between survival rate and year, sample size or Human Development Index. Survival rates, especially the 5-year survival rate in the EMRO were less than European countries and the USA. Documented and comprehensive evidence-based findings of the present meta-analysis can be used to enhance policies and outcomes of different medical areas including prophylaxis, treatment and health related objectives in colorectal 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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".