A comprehensive systematic review and meta-analysis of uterine cancer in Asian countries.
Bibliographic record
Abstract
Background: Uterine cancer ranks among the leading causes of mortality in women, particularly prevalent in countries with low to moderate income levels. Present treatment and healthcare success rates are assessed by the survival rate index. This study aimed to determine the uterine cancer survival rate in Asia. Methods: Five international databases were analyzed to perform this systematic review: Medline/PubMed, ProQuest, Scopus, Web of Knowledge, and Google Scholar, until the end of August 2021. The Newcastle-Ottawa quality assessment form was utilized in the evaluation of quality for cohort studies. "I2 statistic and Cochran test were used to check the analysis process and assess the heterogeneity among the studies. Also, the study year was used as the basis for a meta-regression analysis. Results: The study covered 75 papers in total. The survival rates of uterine cancer after one, three, five, and seven years are 76.68% (95% CI, 66.76-78.61), 63.56% (95% CI, 58.60-68.37), 59.04% (95% CI, 55.62-62.43), and 57.86% (95% CI, 51.16-64.42) according to the random model. Furthermore, according to the outcomes of the meta-regression, there was no correlation found between the study year and the survival rate. Conclusions: Compared to European and American countries, Asian countries have a poorer uterine cancer survival rate, which makes it crucial to improve the survival rate of patients through ensuring early diagnosis of the disease in its early stages and providing new diagnostic methods, modified surgical techniques, and targeted therapies.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".