Self-Screening for Rectal Sexually Transmitted Infections: Human Papillomavirus
Bibliographic record
Abstract
Sir—Reports from North America and Europe confirm a resurgence of bacterial sexually transmitted infections (STIs) among men who have sex with men (MSM). The majority of rectal infections are asymptomatic; therefore, control of these infections necessitates screening. Despite published guidelines for routine screening of MSM for STIs, studies and anecdotal reports alike indicate exceedingly low coverage [1]. In an era in which rectal bacterial STIs are understood to enhance transmission of HIV-1, novel approaches to increase the coverage of rectal screening for STIs among MSM are urgently needed. Very recent improvements in the performance of nucleic acid amplification tests permit consideration of self-collection as a promising approach to increasing rectal STI screening among MSM. However, data pertaining to the suitability of self-collected rectal specimens for accurate detection of STI are as yet exceedingly rare. As part of a larger head-to-head comparison of self-screening versus clinician-performed screening for anal cancer precursor lesions in 222 young MSM [2], we selected for initial human papillomavirus (HPV) typing 24 patients with a diagnosis of atypical squamous cells of undetermined significance and 48 control subjects with normal cytological findings. Here we report the pair-wise concordance of self-collected and clinician-collected specimens for detection of specific HPV types.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.044 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".