HAART slows progression to anal cancer in HIV-infected MSM
Bibliographic record
Abstract
OBJECTIVE: Antiretrovirals do not prevent anal intraepithelial neoplasia. However, the influence of antiretrovirals in the natural history of invasive anal cancer is less clear. The objective is to investigate the impact of antiretrovirals in the time to the development of anal cancer in HIV-positive MSM. DESIGN: A retrospective analysis of cases of anal cancer in a cohort of HIV-positive MSM receiving antiretrovirals between 1988 and 2008. METHODS: Time from first CD4 cell count or HIV RNA viral load test to anal cancer diagnosis was analysed using Cox regression and Kaplan-Meier curves. Anal cancer cases treated in the era prior to HAART (<1996) were compared with those treated later (1996-2008). RESULTS: Anal cancer cases (n = 37) were compared with a cohort of 1654 HIV-positive MSM on antiretrovirals. Antiretrovirals were started in the pre-HAART era by 70% of cancer cases, and median CD4 cell count nadir was 70 cells/μl (10-130). Time to development of anal cancer was shorter for cases treated during the pre-HAART era [adjusted hazard ratio (AHR) 3.04, 95% confidence interval (95% CI) 1.48-6.24, P = 0.002], with a CD4 cell count nadir less than 100 cells/μl (AHR 2.21, 95% CI 1.06-4.62, P = 0.035) and longer duration of CD4 cell count less than 100 cells/μl (AHR 1.33, 95% CI 1.11-1.58, P = 0.002). CONCLUSION: Results show that severe immunosuppression and starting therapy pre-HAART are associated with an increased risk of anal cancer. HIV-positive MSM initiating antiretrovirals during the HAART era (1996-2008) had a longer time to the development of anal cancer than those treated pre-HAART. Our results suggest that early use of HAART may delay progression to anal cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".