Prevalence and determinants of late-stage presentation among cervical cancer patients, a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: An estimated 570,000 women were diagnosed with cervical cancer worldwide, and about 311,000 women died from the disease. Cervical cancer is possibly the most curable human cancer; if detected at the precancerous stage. Additionally, early diagnosis and management other factors are essential to decrease mortality rate among those patients. So this review was aimed to identify the prevalence and determinants of late-stage presentation among cervical cancer patients. METHODS: A systematic search had carried out on PubMed, EMBASE, MEDLINE, Cochrane, Scopus, Web of Science CINAHL, and manually on Google Scholar. This meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The modified Newcastle-Ottawa Scale (NOS) was used to assess the quality of each study. A meta-analysis was done using a random-effects method using the STATA™ Version 14 software. RESULT: Twenty-five studies from 3 world regions with 53,233 participants were enrolled in this meta-analysis. The overall estimated global pooled prevalence of late-stage presentation among cervical cancer patients with a random-effects model was 60.66% (95% CI: 56.27, 65.06). The subgroup analysis revealed that the prevalence of late-stage presentation was 62.60% in Africa, 69.30% in Asia, 46.51% in Europe, and 50.16% in North America. Educational status (p = 0.031) and place of residence (p = 0.004) are determinants of late-stage presentation. CONCLUSION: The results of this meta-analysis indicated that the prevalence of late-stage presentation of cervical cancer is substantially high. Place of residence and educational status were significantly associated with late-stage presentation. Health care organizations should work on early screening, management, and on increasing community awareness to minimize late stage at presentation among those patients.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| 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".