Exploring How Epidemic Context Influences Syphilis Screening Impact: A Mathematical Modeling Study
Bibliographic record
Abstract
BACKGROUND: The current syphilis epidemic in the United States is concentrated in gay, bisexual, and other men who have sex with men (MSM), but substantial heterosexual transmission is reported in some parts of the country. Using the US states of Louisiana and Massachusetts as case studies, we investigated how epidemic context influences the impact of population screening approaches for syphilis control. METHODS: We constructed a compartmental metapopulation model parameterized to describe observed patterns of syphilis transmission. We estimated the impact of different approaches to screening, including perfect adherence to current US screening guidelines in MSM. RESULTS: In Louisiana, where syphilis cases are more evenly distributed among MSM and heterosexual populations, we projected that screening according to guidelines would contribute to no change or an increase in syphilis burden, compared with burden with current estimated screening coverage. In Massachusetts, which has a more MSM-focused outbreak, we projected that screening according to guidelines would be as or more effective than current screening coverage in most population groups. CONCLUSIONS: Men who have sex with men-focused approaches to screening may be insufficient for control when there is substantial transmission in heterosexual populations. Epidemic characteristics may be useful when identifying at-risk groups for syphilis screening.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".