A meta‐analysis of anal cancer incidence by risk group: Toward a unified anal cancer risk scale
Bibliographic record
Abstract
Certain population groups are known to have higher than average anal cancer risk, namely persons living with HIV (PLHIV), men who have sex with men (MSM), women diagnosed with human papillomavirus (HPV)-related gynecological precancerous lesions or cancer, solid organ transplant recipients (SOTRs) and patients with autoimmune diseases. Our aim was to provide robust and comparable estimates of anal cancer burden across these groups. Summary incidence rates (IRs), as cases per 100 000 person-years (py), were calculated by fixed-effects meta-analysis. IRs were 85 (95% confidence interval [CI] = 82-89) for HIV-positive MSM (n = 7 studies; 2 229 234 py), 32 (95% CI = 30-35) for non-MSM male PLHIV (n = 5; 1626 448 py) and 22 (95% CI = 19-24) for female PLHIV (n = 6; 1 472 123 py), with strong variation by age (eg, from 16.8 < 30 years to 107.5 ≥ 60 years for HIV-positive MSM). IR was 19 (95% CI = 10-36) in HIV-negative MSM (n = 2; 48 135 py). Anal cancer IRs were much higher after diagnosis of vulvar (IR = 48 [95% CI = 38-61]; n = 4; 145 147 py) than cervical (9 [95% CI = 8-12]; n = 4; 779 098 py) or vaginal (IR = 10 [95% CI = 3-30]; n = 4; 32 671) cancer, with equivalent disparity after respective precancerous lesions. IR was 13 (95% CI = 12-15) in SOTRs (n = 5; 1 946 206 py), reaching 24.5 and 49.6 for males and females >10 years after transplant. Anal cancer IRs were 10 (95% CI = 5-19), 6 (95% CI = 3-11) and 3 (95% CI = 2-4) for systemic lupus erythematosus, ulcerative colitis and Crohn's disease, respectively. In conclusion, a unifying anal cancer risk scale, based upon comprehensive meta-analysis, can improve prioritization and standardization in anal cancer prevention/research initiatives, which are in their public health infancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.061 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".