Association of female genital schistosomiasis and human papillomavirus and cervical pre-cancer: a systematic review
Bibliographic record
Abstract
Abstract Background S. haematobium is a recognized carcinogen and is associated with squamous cell carcinoma of the bladder. Its association with high-risk(HR) human papillomavirus (HPV) persistence, cervical pre-cancer and cervical cancer incidence has not been fully explored. Methods We searched OvidSP MEDLINE, OvidSP Embase, Global Index Medicus, PubMed and the Wiley Cochrane library without date or language restrictions up to April 20, 2024 for abstracts evaluating the association of female genital schistosomiasis (FGS) with the prevalence, incidence or persistence of cervical HR-HPV, and incidence of histology-verified cervical pre-cancer or cancer. Cervical pre-cancer defined using cervical cytology or visual inspection with acetic acid (VIA) was also considered, but as lower quality evidence. We assessed the risk of bias of included studies using a modified Newcastle Ottawa scale. This study is registered on PROSPERO: CRD42023389301. Results We identified 1,170 publications and six studies were eligible for inclusion. Five studies were cross sectional and 1 was prospective. The studies describe 1081 women living in sub-Saharan Africa. One study from Zimbabwe reported an increased risk of HR-HPV prevalence at baseline in women with composite-FGS compared to women without FGS (aOR 1.9, 95% CI 1.1 – 3.6, p = 0.03), however no association was seen after 5 years of follow-up. Another study from KwaZulu-Natal reported an increased odds of any HPV prevalence among women with visual-FGS compared to women without FGS (aOR 1.71 [1.14 – 2.56], p = 0.01). However, a study in Madagascar did not show increased odds of any HPV among women with visual-FGS compared to women without FGS (OR 1.0 [0.82 – 1.2). Of 4 studies evaluating the association of FGS and cervical pre-cancer, one reported an increased risk of VIA abnormalities in women with molecular-FGS compared to those without (aOR 6.08, 95% CI 1.58 – 23.37). Three studies did not report an association between FGS and cervical pre-cancer (cytology defined (n = 2) and histology defined (n = 1)). Conclusion There are limited and low quality data on the risk of HR-HPV infection and cervical pre-cancer and cancer among women with FGS. Given limited data, it was not possible to confirm or exclude an association between FGS and HPV, cervical pre-cancer, and cervical cancer and additional research is needed.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".