Environmental scan of anal cancer screening practices: worldwide survey results
Bibliographic record
Abstract
Anal squamous cell carcinoma is rare in the general population but certain populations, such as persons with HIV, are at increased risk. High-risk populations can be screened for anal cancer using strategies similar to those used for cervical cancer. However, little is known about the use of such screening practices across jurisdictions. Data were collected using an online survey. Health care professionals currently providing anal cancer screening services were invited to complete the survey via email and/or fax. Information was collected on populations screened, services and treatments offered, and personnel. Over 300 invitations were sent; 82 providers from 80 clinics around the world completed the survey. Fourteen clinics have each examined more than 1000 patients. Over a third of clinics do not restrict access to screening; in the rest, eligibility is most commonly based on HIV status and abnormal anal cytology results. Fifty-three percent of clinics require abnormal anal cytology prior to performing high-resolution anoscopy (HRA) in asymptomatic patients. Almost all clinics offer both anal cytology and HRA. Internal high-grade anal intraepithelial neoplasia (AIN) is most often treated with infrared coagulation (61%), whereas external high-grade AIN is most commonly treated with imiquimod (49%). Most procedures are performed by physicians, followed by nurse practitioners. Our study is the first description of global anal cancer screening practices. Our findings may be used to inform practice and health policy in jurisdictions considering anal cancer screening.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".