Association of <scp>HIV</scp> viral load and <scp>CD</scp>4 cell count with human papillomavirus detection and clearance in <scp>HIV</scp>‐infected women initiating highly active antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVES: The extent to which highly active antiretroviral therapy (HAART) affects human papillomavirus (HPV) acquisition and clearance in HIV-infected women is not well understood. We sought to describe high-risk HPV detection and clearance rates over time since HAART initiation, based on time-varying HIV viral load (VL) and CD4 T-cell count, using novel statistical methods. METHODS: We conducted a retrospective analysis of data from the completed AIDS Clinical Trials Group (ACTG) A5029 study using multi-state Markov models. Two sets of high-risk HPV types from 2003 and 2009 publications were considered. RESULTS: There was some evidence that VL>400 HIV-1 RNA copies/mL was marginally associated with a higher rate of HPV detection [P=0.068; hazard ratio (HR) =4.67], using the older set of high-risk HPV types. Such an association was not identified using the latest set of HPV types (P=0.343; HR=2.64). CD4 count >350 cells/μL was significantly associated with more rapid HPV clearance with both sets of HPV types (P=0.001, HR=3.93; P=0.018, HR=2.65). There was no evidence that HPV affects VL or CD4 cell count in any of the analyses. CONCLUSIONS: High-risk HPV types vary among studies and can affect the results of analyses. Use of HAART to improve CD4 cell count may have an impact on the control of HPV infection. The decrease in VL may also have an effect, although to a lesser degree.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".