Effect of CD4 <sup>+</sup> T Cell Count and Antiretroviral Treatment on Two Serological HIV Incidence Assays
Bibliographic record
Abstract
Serological assays are increasingly being used to measure HIV incidence in cross-sectional studies, but their specificity to determine incident infections remains problematic. We estimated the specificity of the BED assay in a cohort of long-term HIV-infected adults before and during antiretroviral treatment (ART) and evaluated an HIV avidity assay to detect BED-based false-recent results. We used the BED assay to test stored specimens from known long-term HIV-1-infected adult Ugandans before and at 3, 12, and 24 months after ART initiation. We evaluated the frequency of false-recent classifications by ART status and CD4(+) T(+) cell count. Specimens classified as BED false-recent were further tested with an avidity assay. In all, 950 blood specimens from 253 adults were tested with the BED assay. Of these, 149 (15.7%) specimens tested false-recent and 64 (24.9%) individuals tested false-recent at least once. Among all specimens tested, the proportion of false-recent rose with increasing CD4(+) cell count (<250 cells/μl: 11.3%, 250-499: 17.8%, ≥500: 21.4%; p for trend=0.002). Of 197 persons with all four BED results available, 75.6% were classified as long-term infected throughout and 8.1% as false-recent throughout; the remainder changed classification once (12.2%) or twice (4.1%). Of 105 false-recent specimens retested with the avidity assay, 101 (96.2%) were correctly classified as "long-term." The BED assay's specificity varied with CD4(+) cell count and use of ART. Knowledge of these parameters for blood samples could improve incidence estimates using the BED assay. The additional use of an avidity assay may help to minimize the proportion of BED false-recent specimens.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".