Survival rate of vaginal cancer in Asian countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Vaginal cancer is one of the major causes of mortality in women, which mostly takes place in low- and middle-income countries. Assessing the survival rate of vaginal cancer is essential to investigate the success rate of current treatments and screening tools. This study aims to determine the survival rate of vaginal cancer in Asia. Methods: This systematic review was carried out using four international databases, including Medline/Pubmed, ProQuest, Scopus, Web of Knowledge, and also Google Scholar. Articles were investigated up to the end of August 2021. The authors utilized the Newcastle–Ottawa Scale to evaluate the quality of the articles. Evaluating the papers for heterogeneity was performed using the Cochrane test and I² statistic. Meta-regression analysis was also applied based on the year of the study. Results: Three articles (13 records) fulfilled the inclusion criteria. Based on the random model, the overall 5-year survival rate was 74.63%. Also, the rates of survival in relation to the type of treatment including chemotherapy, radiotherapy, or other modalities, were 78.53, 78.44, and 68.54%, respectively. According to meta-regression analysis, no correlation was found between the survival rate and the year of the study. Conclusion: The vaginal cancer survival rate is lower in Asian countries compared to that of developed countries. Increasing patient survival rates in such countries is crucial by implementing newer diagnostic tools, advanced surgical techniques, and goal-oriented treatments. Early diagnosis in lower stages and educating the populations about risk factors and preventative measures are also necessary for raising the rate of survival.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 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 | 0.002 |
| 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".