Resurgent Syphilis Across the Globe: A Public Health Perspective on Bridging Surveillance and Strategy
Bibliographic record
Abstract
Syphilis, a curable sexually transmitted infection, has resurged globally, challenging public health systems in both high-income countries and low- and middle-income countries (LMICs). In nations like the United States, the United Kingdom, parts of Europe, Canada, and Japan, cases have surged due to declining condom use, digital platforms facilitating casual sex, and practices like chemsex and broader drug use for sex, with rising congenital syphilis rates. In LMICs, such as those in East Africa, South Asia, Latin America, and Southeast Asia, limited healthcare access, inadequate prenatal screening, and socioeconomic barriers drive persistent high prevalence, particularly among pregnant women and vulnerable populations. Despite contextual differences, shared drivers include stigma, health disparities, and outdated surveillance systems. This resurgence underscores the need for globally coordinated, equity-focused strategies, including universal syphilis testing, modernized surveillance, and context-specific sexual health education. Addressing structural and behavioral factors through collaborative international efforts is critical to reversing this trend and strengthening global STI control.
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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".