Secondary Prevention of Anal Cancer among Men living with HIV: Acceptability and Suitability of Anal Pap Cytology and HPV DNA Type Testing
Bibliographic record
Abstract
The risk of anal cancer is magnitudes higher among men living HIV than the general population. To contribute to an evidence-base in support of anal cancer screening guidelines, this dissertation evaluates the acceptability of screening among men living with HIV and sets the foundation for work examining the suitability of anal cytology and HPV DNA type testing for screening this population. A sample of 1677 men living with HIV enrolled in the Ontario HIV Treatment Network Cohort study completed a questionnaire module on HPV, its associated diseases, and their prevention in 2016-2017. Three objectives examined acceptability of anal cancer screening by 1) quantifying prevalence of screening, 2) examining the role of past screening on beliefs and willingness to be screened, and 3) evaluating facilitators to screening, HPV knowledge and self-perceived risk for anal cancer. In this sample, disparities in anal cancer screening were observed, wherein men from some racialized groups and heterosexual men were less likely to have discussed screening or to have been screened. The vast majority (90%) of men, however, were willing to undergo screening in the future. Positive beliefs regarding screening were associated with higher willingness. Behavioural intention does not ensure uptake, however, and factors such as health literacy and perceived susceptibility can influence men’s participation. Half of the study sample were unaware of HPV and the majority (73%) felt they had no or low chance of getting anal cancer. Specific risk-focused knowledge was associated with higher perceptions of risk. Findings will inform community-led health literacy efforts to improve participation in anal cancer screening. The final objective provides methodological guidance for the analysis of screening studies when verification of screening results is not possible for all participants. This simulation study assessed the utility of statistical methods to account for verification bias that arises in these scenarios. Findings suggest multiple imputation may be most suitable for handling complex missing data scenarios common to screening studies. This work will inform the analysis of data from a forthcoming anal cancer screening study to determine the best algorithm by which to screen for anal cancer among men living with HIV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".