Increased regression and decreased incidence of human papillomavirus-related cervical lesions among HIV-infected women on HAART
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of HAART on incidence, regression, and progression of cytopathological abnormalities in HIV-infected women. DESIGN: Prospective cohort. METHODS: HIV-infected women (N=1123) from Soweto, South Africa underwent serial cervical smears that were analyzed and reported using the Bethesda System. The results of HAART and non-HAART users were compared using two statistical approaches: a survival analysis assessing risk of incident smear abnormality among women with baseline normal smear results; and analysis with marginal models assessing for an association between HAART use and likelihood of regression/progression in consecutive smears. RESULTS: After multivariate survival analysis, women using HAART with a normal baseline smear were 38% less likely to have an incident smear abnormality during follow-up than nonusers [confidence interval (CI) 0.42-0.91; P=0.01]. Multivariate marginal models analysis identified a significantly increased likelihood (odds ratio 2.61; CI 1.75-3.89; P<0.0001) of regression of cervical lesions among women on HAART. CONCLUSION: Our large prospective cohort study adds significant weight to the side of the balance of clinical research supporting the positive impact of HAART on the natural history of human papillomavirus-related cervical disease in HIV-infected women.
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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".