Can enhanced screening of men with a history of prior syphilis infection stem the epidemic in men who have sex with men? A mathematical modelling study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to determine the transmission impact of using prior syphilis infection to guide a focused syphilis screening intervention among men who have sex with men (MSM). METHODS: We parameterised a deterministic model of syphilis transmission in MSM to reflect the 2011-2015 syphilis outbreak in Winnipeg, Canada. Enhanced screening of 75% of men with prior syphilis every 3 months (A) was compared with distributing equivalent number tests to all MSM (B) or those with the highest partner number (C). We compared early syphilis incidence, diagnoses and prevalence after 10 years, relative to a base case of 30% of MSM screened annually. RESULTS: Strategy A was expected to avert 52% of incident infections, 44% of diagnosed cases and reduce early syphilis prevalence by 89%. Strategy B had the least impact. Strategy C was most effective, averting 59% of incident cases. When screening frequency was semiannual or annual, strategy A was the most effective. CONCLUSIONS: Enhanced screening of MSM with prior syphilis may efficiently reduce transmission, especially when identification of high-risk men via self-reported partner numbers or high-frequency screening is difficult to achieve.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".