Now, more than ever, it’s time to address the neglect of female genital schistosomiasis
Bibliographic record
Abstract
, resulting in trapped parasite eggs in the genital tract, causes lesions that mimic sexually transmitted infections and cervical neoplasia, often leading to misdiagnosis, stigma and delayed treatment. This review summarises current developments on FGS burden, prevention, diagnostics, integration, policy, community engagement and identifies critical threats to progress. Ongoing surveys show promise in ensuring robust burden estimates and age-related risk data. Diagnostic advances include portable colposcopy, digital image analysis techniques and molecular assays, although limitations persist in resource-limited settings. Praziquantel remains the cornerstone of treatment, yet single-dose regimens inadequately reverse established lesions; repeated dosing shows improved parasite clearance but limited lesion regression, highlighting the necessity for early, life-course preventive chemotherapy including access to paediatric praziquantel. Successful programmatic pilots have developed training curricula, minimum service packages, community engagement tools and have integrated FGS care into SRH platforms. Policy momentum is building through World Health Organization taskforces and national strategies, yet sustainable financing remains a challenge. Key threats include bilateral aid reductions, climate change, emerging infections, rising healthcare costs and persistent gender inequities. To address these challenges, we propose seven priority actions, encompassing all health system building blocks, for the global community. Nationally coordinated, multisectoral efforts are urgently required to embed FGS prevention, diagnosis and management within broader health systems, thereby improving outcomes for affected women and girls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".