Pattern and Frequency of Seroreactivity to Routinely Used Serologic Tests in Early-Treated Infants With HIV
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown low frequencies of seroreactivity to HIV diagnostic assays for infected infants treated with antiretroviral therapy (ART) early in infection. METHODS: Fifty-eight HIV-infected infants treated with ART at a median age of 1.9 months (range: 0.2-5.4) for up to 4 years of life were assessed for seroreactivity to 4 routinely used HIV clinical immunoassays (IA): Second-generation (2ndG) IA and 2 rapid diagnostic tests (RDT), based on third-generation principles, measuring antibody only and a fourth-generation (4thG) antigen/antibody IA. HIV Western blot assay was also performed to assess HIV-specific antibodies. RESULTS: The 2ndG IA demonstrated the highest frequency of seroreactivity in children (69%) followed by the 4thG IA (40%) and the RDT (26%) after one year of ART. Infants initiating ART during ages 3-6 months (N = 15) showed a greater frequency (range: 53%-93%) and breadth (median and range: 3 [1-4]) of reactivity across the assays compared with those treated within 3 months (N = 43):16%-61% and breadth (1 [0-4]). The 4thG IA showed significantly reduced reactivity relative to the 2ndG IA at one (P = 0.016) and 3 (P = 0.004) years of ART. Western blot profiles following 3 years of ART showed the highest frequency of reactivity to HIV Gag p24 (76%) and lowest reactivity to Env gp120 and gp41, with only 24% of children confirmed positive by the assay. CONCLUSIONS: These results suggest that the use of 4thG IA and RDT test combination algorithms with limited HIV antigen breadth may not be adequate for diagnosis of HIV-infected children following early treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".