No Difference in Keratin Thickness between Inner and Outer Foreskins from Elective Male Circumcisions in Rakai, Uganda
Bibliographic record
Abstract
It has been hypothesized that increased HIV acquisition in uncircumcised men may relate to a more thinly keratinized inner foreskin. However, published data are contradictory and potentially confounded by medical indications for circumcision. We tested the hypothesis that the inner foreskin was more thinly keratinized than the outer foreskin using tissues from 19 healthy, HIV-uninfected men undergoing routine prophylactic circumcision in Rakai, Uganda. Sections from 3 foreskin anatomic sites (inner, outer, and frenar band) were snap-frozen separately. Two independent laboratories each separately stained, imaged, and measured keratin thicknesses in a blinded fashion. There was no significant difference in keratin thickness between the inner (mean = 14.67±7.48 µm) and outer (mean = 13.30±8.49 µm) foreskin, or between the inner foreskin and the frenar band (mean = 16.91±12.42 µm). While the frenar band showed the greatest intra-individual heterogeneity in keratin thickness, there was substantial inter-individual variation seen in all regions. Measurements made by the two laboratories showed high correlation (r = 0.741, 95% CI, 0.533-0.864). We conclude that, despite inter- and intra-individual variability, keratin thickness was similar in the inner and outer foreskin of healthy Ugandan men, and that reduced keratin thickness is not likely to make the inner foreskin more susceptible to HIV acquisition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".