Circumcision to prevent HIV and other sexually transmitted infections in men who have sex with men: a systematic review and meta-analysis of global data
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) are disproportionately affected by HIV and other sexually transmitted infections (STIs) worldwide. Previous reviews investigating the role of circumcision in preventing HIV and other STIs among MSM were inconclusive. Many new studies have emerged in the past decade. To inform global prevention strategies for HIV and other STIs among MSM, we reviewed all available evidence on the associations between circumcision and HIV and other STIs among MSM. METHODS: In this systematic review and meta-analysis, we searched PubMed, Web of Science, BioMed Central, Scopus, ResearchGate, Cochrane Library, Embase, PsycINFO, Google Scholar, and websites of international HIV and STI conferences for studies published before March 8, 2018. Interventional or observational studies containing original quantitative data describing associations between circumcision and incident or prevalent infection of HIV and other STIs among MSM were included. Studies were excluded if MSM could not be distinguished from men who have sex with women only. We calculated pooled odds ratios (ORs) and their 95% CIs using random-effect models. We assessed risk of bias using the Newcastle-Ottawa scale. FINDINGS: =0%). INTERPRETATION: We found evidence that circumcision is likely to protect MSM from HIV infection, particularly in countries of low and middle income. Circumcision might also protect MSM from HSV and penile HPV infection. MSM should be included in campaigns promoting circumcision among men in countries of low and middle income. In view of the substantial proportion of MSM in countries of low and middle income who also have sex with women, well designed longitudinal studies differentiating MSM only and bisexual men are needed to clarify the effect of circumcision on male-to-male transmission of HIV and other STIs. FUNDING: National Natural Science Foundation of China, National Science and Technology Major Project of China, Australian National Health and Medical Research Council Early Career Fellowship, Sanming Project of Medicine in Shenzhen, National Institutes of Health, Mega Projects of National Science Research for the 13th Five-Year Plan, Doris Duke Charitable Foundation.
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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".