Survival rate of cervical cancer in Asian countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objective Cancer is one of the main causes of death, and cervical cancer is the fourth most common cancer and the fourth leading cause of death from malignancy among women. Knowing the survival rate is used to evaluate the success of current treatments and care. This study was conducted to assess the survival rate of cervical cancer in Asia. Methods This systematic survey was conducted on four international databases, including Medline/PubMed, ProQuest, Scopus, and Web of Knowledge, and includes manuscripts that were published until the end of August 2021. Selected keywords were searched for international databases including cervical neoplasms [mesh], survival analysis or survival or survival rate, Asian countries (name of countries). The Newcastle-Ottawa Qualitative Evaluation Form was used for cohort studies to evaluate the quality of the articles. The analysis process was performed to evaluate the heterogeneity of the studies using the Cochran test and I2 statistics. Additionally, a meta-regression analysis was performed based on the year of the study. Results A total of 1956 articles were selected and reviewed based on their title. The results showed that 110 articles met the inclusion criteria. According to the randomized model, the 1, 3, 5, and 10-year survival rates of cervical cancer were 76.62% (95% Confidence Interval (CI), 72.91_80.34), 68.77% (95% CI, 64.32_73.21), 62.34% (95% CI, 58.10_66.59), and 61.60% (95% CI, 52.31_70.89), respectively. Additionally, based on the results of meta-regression analysis, there was an association between the year of the study and the survival rate, elucidating that the survival rate of cervical cancer has increased over the years. Conclusions Results can provide the basic information needed for effective policy making, and development of public health programs for prevention, diagnosis, and treatment of cervical 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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.003 | 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".