Circumcision and non-HIV sexually transmitted infections
Bibliographic record
Abstract
As urologists, we are frequently asked questions about the foreskin by colleagues, patients and their families, as well as our own friends and family members. It seems not a month passes without a statement in the lay-press identifying a benefit to circumcision with respect to STI transmission in the developing world. We are then asked if this information applies to babies in Canada. Then the debate begins again about the pros and cons of circumcision. In 2007, CUAJ published a spirited point-counterpoint article on the pros and cons of newborn circumcision.1,2 This opinion piece is a follow-up to that article, with a focus on emerging data regarding the impact of circumcision on the transmission of non-HIV sexually transmitted infections (STIs). Evidence supporting the effectiveness of adult circumcision for the reduction of HIV acquisition in men is strong and is based on several randomized controlled trials performed in the developing world.3–5 However, until recently, the same could not be said regarding other STIs. This was mainly due to the lack of randomized trials. The core of our knowledge surrounding the relationship between circumcision and non-HIV STIs stems from observational studies that are prone to bias and confounder effects. Common examples of the flaws in existing studies include the variety of methods of ascertaining the exposure or the outcomes, inclusion of diverse patient populations (geographically, culturally, baseline risk), and differing ages of circumcision (i.e., before or after sexual debut). Consequently, there are conflicting results among studies which reflect the heterogeneity seen in all systematic reviews on this topic. That said, a summary of our observations may prove useful for the purposes of patient and physician education.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".