Survival rate of pancreatic cancer in Asian countries: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: So far, no comprehensive study has been conducted regarding the survival rate of pancreatic cancer patients in Asia; Therefore, according to the mentioned points, the present study was designed with the aim of conducting a systematic review to calculate the survival rate of pancreatic cancer in Asian countries in 2024. METHODS: This research is a systematic review and meta-analysis. The researchers of this study examined the articles published in 5 international databases including Medline/PubMed, Scopus, Google Scholar, Web of knowledge and ProQuest. Preparation for data analysis was based on content analysis of the Newcastle-Ottawa Quality Assessment Form. Due to the existence of heterogeneity (with a significance level of <0.1 and I2 >50%), the random effects model was used with the reverse variance method. Meta-regression was also performed for the HDI index. All analyzes were performed by CMA version 2 statistical software. RESULTS: Finally, 27 articles were selected to enter the meta-analysis model. The 1-year survival rate of pancreatic cancer was estimated to be 27.6% (95% CI: 24.2%-31.2%). The 5-year survival rate for both sexes was 9.7% (95% CI: 8.8%-10.6%). The estimated 5-year survival rates in men and women were 9.3% (95% CI:8.3%-10.4%) and 10.4% (95% CI: 10.2%-10.7%), respectively. The results of the meta-regression model showed that there is a significant relationship between Human Development Index (HDI) and pancreatic cancer survival rate (point estimate of slope: 4.73, standard error: 0.11, P-value < .001). CONCLUSION: Overall, the 1- and 5-year survival rates for pancreatic cancer in Asian countries were 27.6% and 9.7%, respectively. This rate was lower than in the United States. It seems that the design, implementation, monitoring and planning of integrated diagnostic and treatment services in Asian countries can greatly increase the survival of pancreatic cancer in the coming decades.
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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) | 0.015 | 0.045 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.002 | 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".