Swab‐based anal cancer screening in men living with <scp>HIV</scp>: Projected outcomes for different screening algorithms
Bibliographic record
Abstract
Screening for and treatment of anal cancer precursor lesions, high-grade squamous intraepithelial lesions (HSIL), can prevent anal cancer. Recent guidelines set by the International Anal Neoplasia Society recommend digital anal rectal examination (DARE) and anal swab-based screening of high-risk individuals by means of high-risk (hr)HPV testing or cytology. We used our biobank containing data of more than 600 high-resolution anoscopy (HRA) screened participants (94% men with HIV) to compare the possible screening algorithms. We selected the 298 screening participants in whom anal swabs were successfully tested for hrHPV and cytology, parallel to HRA screening (DARE followed by complete visual inspection by HRA). We compared outcomes of several strategies (single-test, co-testing, two-step testing) with one or two positive tests required for HRA referral, resulting in 20 possible screening algorithms. We also assessed the sensitivity of DARE to detect anal cancer. We found that the percentage of missed HSIL was lowest with hrHPV testing, either alone (14.2%) or combined with cytology (≥ASCUS threshold: 4.4%; HSIL threshold: 8.8%) (co-testing or two-step testing, with ≥1 positive test required for HRA referral). Using these screening algorithms, 61.0 %, 79.0 %, and 63.7% of the participants were referred for HRA. While in some scenarios a small percentage of cancers was missed, all were detected by DARE. Whatever strategy is chosen, screening outcomes will have to be monitored closely to evaluate the program and make adaptations when necessary.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".