The Global Prevalence of Schistosoma mansoni, S. japonicum, and S. haematobium in Pregnant Women: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Schistosomiasis is a neglected tropical disease affecting 40 million women of childbearing age worldwide. Its global disease prevalence among pregnant women is still unknown. This meta-analysis determined the pooled prevalence of schistosomiasis among pregnant women globally. Additionally, this study also determined the pooled prevalence based on infection intensity based on eggs per gram. Observational studies on the prevalence of schistosomiasis among pregnant patients were obtained from Medline, Scopus, and CINAHL from January 2001 until August 2020. A review of titles and abstracts was done independently by six reviewers. The quality of the included studies was assessed using the Newcastle–Ottawa Scale for case–control, cohort, and cross-sectional studies. A total of 27 studies were included in the meta-analysis and meta-regression. The pooled prevalence of S. haematobium was 13.44 (CI: 8.90–19.80) per 100 observations, while the pooled prevalence of S. mansoni was 12.18 (CI: 4.47–29.12) per 100 observations. The prevalence of S. japonicum infection in one study was 53.54 (CI: 43.23–63.62) per 100 observations. Our results showed a prevailing health problem of schistosomiasis during pregnancy in various countries worldwide. This strengthens the need to conduct more schistosomiasis research, prevention, and control programs in pregnant women.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.010 | 0.011 |
| 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".