Optimizing Coverage vs Frequency for Sexually Transmitted Infection Screening of Men Who Have Sex With Men
Bibliographic record
Abstract
BACKGROUND: The incidence of bacterial sexually transmitted infections (STIs) in men who have sex with men (MSM) has increased substantially despite availability of effective antibiotics. The US Centers for Disease Control and Prevention (CDC) recommends annual screening for all sexually active (SA) MSM and more frequent screening for high-risk (HR) MSM. The population-level benefits of improved coverage vs increased frequency of STI screening among SA vs HR MSM are unknown. METHODS: We used a network transmission model of gonorrhea (NG) and chlamydia (CT) among MSM to simulate the implementation of STI screening across different scenarios, starting with the CDC guidelines at current coverage levels. Counterfactual model scenarios varied screening coverage and frequency for SA MSM and HR MSM (MSM with multiple recent partners). We estimated infections averted and the number needed to screen to prevent 1 new infection. RESULTS: Compared with current recommendations, increasing the frequency of screening to biannually for all SA MSM and adding some HR screening could avert 72% of NG and 78% of CT infections over 10 years. Biannual screening of 30% of HR MSM at empirical coverage levels for annual SA screening could avert 76% of NG and 84% of CT infections. Other scenarios, including higher coverage among SA MSM and increasing frequency for HR MSM, averted fewer infections but did so at a lower number needed to screen. CONCLUSIONS: The optimal screening scenarios in this model to reduce STI incidence among MSM included more frequent screening for all sexually active MSM and higher coverage of screening for HR men with multiple partners.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.004 | 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".