A Cut Above the Rest
Bibliographic record
Abstract
BACKGROUND: Randomized clinical trials have shown that medical male circumcision substantially reduces the risk of contracting HIV. However, relatively little is known about the relationship between traditional male circumcision and HIV risk. This article examines variations in traditional circumcision practices and their relationship to HIV status. METHODS: We used data from the fifth wave of the Cape Area Panel Study (n = 473) of young adults in Cape Town, South Africa, to determine attitudes towards circumcision, whether men were circumcised, at what age, and whether their foreskin had been fully or partially removed. Probit models were estimated to determine the association between extent and age of circumcision and HIV status. RESULTS: There was strong support for traditional male circumcision. 92.5% of the men reported being circumcised, with 10.5% partially circumcised. Partially circumcised men had a 7% point greater risk of being HIV positive than fully circumcised men (P < 0.05) and equal risk compared with uncircumcised men. Most (91%) men were circumcised between the ages of 17 and 22 years (mean 19.2 years), and HIV risk increased with age of circumcision (P < 0.10). CONCLUSIONS: Efforts should be made to encourage earlier circumcisions and to work with traditional surgeons to reduce the number of partial circumcisions. Data on the extent and age of circumcision are necessary for meaningful conclusions to be drawn from survey data about the relationship between circumcision and HIV status.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.210 | 0.078 |
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".