Development and Calibration of a Mathematical Model of Anal Carcinogenesis for High-Risk HIV-Infected Men
Bibliographic record
Abstract
OBJECTIVES: Men who have sex with men who are living with HIV are at highest risk for anal cancer. Our objective was to use empirical data to develop a comprehensive disease simulation model that reflects the most current understanding of anal carcinogenesis, which is uniquely positioned to evaluate future anal cancer screening strategies and provide insight on the unobservable course of the disease. SETTING: North America. METHODS: The individual-based simulation model was calibrated leveraging primary data from empirical studies, such as a longitudinal HIV-positive men who have sex with men cohort study [Human Immunodeficiency and Papilloma Virus Research Group (HIPVIRG); n = 247] and the North American AIDS Cohort Collaboration on Research and Design [(NA-ACCORD); n = 13,146]. We used the model to infer unobservable progression probabilities from high-grade precancer to invasive anal cancer by CD4 nadir and human papillomavirus (HPV) genotype. RESULTS: The calibrated model had good correspondence to data on genotype- and age-specific HPV prevalence; genotype frequency in precancer and cancer; and age- and nadir CD4-specific cancer incidence. The model-projected progression probabilities differed substantially by HPV genotype and nadir CD4 status. For example, among individuals with CD4 nadir <200, the median monthly progression probability from a high-grade lesion to invasive cancer was 0.054% (ie, 6.28% 10-year probability) and 0.004% (ie, 0.48% 10-year probability) for men with an HPV-16 infection versus without a detectable HPV infection, respectively. CONCLUSIONS: We synthesized existing evidence into a state-of-the-art anal cancer disease simulation model that will be used to quantify the tradeoffs of harms and benefits of alternative strategies, understand critical uncertainties, and inform national anal cancer prevention policy.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".