Survival rate of ovarian cancer in Asian countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Ovarian cancer is amongst one of the most commonly occurring cancers affecting women, and the leading cause of gynecologic related cancer death. Its poor prognosis and high mortality rates can be attributed to the absence of specific signs and symptoms until advance stages, which frequently leads to late diagnosis. Survival rate of patients diagnosed with ovarian cancer can be used in order to better assess current standard of care; the aim of this study is to evaluate the survival rate of ovarian cancer patients in Asia. Methods Systematic review was performed on articles that were published by the end of August 2021 in five international databases, including Medline / PubMed, ProQuest, Scopus, Web of Knowledge, and Google Scholar. The Newcastle-Ottawa quality evaluation form was used for cohort studies to evaluate the quality of the articles. The Cochran-Q and I 2 tests were used to calculate the heterogeneity of the studies. The Meta-regression analysis was also done according to when the study was published. Results A total of 667 articles were reviewed, from which 108 were included in this study because they passed the criteria. Based on a randomized model, the survival rates of ovarian cancer after 1, 3 and 5 years were respectively 73.65% (95% CI, 68.66–78.64), 61.31% (95% CI, 55.39–67.23) and 59.60% (95% CI, 56.06–63.13). Additionally, based on meta-regression analysis, there was no relationship between the year of study and survival rate. Conclusions The 1-year survival rate was higher than that of 3- and 5-year for ovarian cancer. This study provides invaluable information that can not only help establish better standard of care for treatment of ovarian cancer, but also assist in development of superior health interventions for prevention and treatment of the disease.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.007 | 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".