Pooled nucleic acid testing increases the diagnostic yield of acute HIV infections in a high-risk population compared to 3rd and 4th generation HIV enzyme immunoassays
Bibliographic record
Abstract
OBJECTIVES: We compared a 3rd generation (gen) and two 4th gen HIV enzyme immunoassays (EIA) to pooled nucleic acid testing (PNAT) for the identification of pre- and early seroconversion acute HIV infection (AHI). STUDY DESIGN: 9550 specimens from males >18 year from clinics attended by men who have sex with men were tested by Siemens ADVIA Centaur(®) HIV 1/O/2 (3rd gen) and HIV Combo (4th gen), as well as by Abbott ARCHITECT(®) HIV Ag/Ab Combo (4th gen). Third gen non-reactive specimens were also tested by Roche COBAS(®) Ampliprep/COBAS® TaqMan HIV-1 Test v.2 in pools of 24 samples. Sensitivity and specificity of the three EIAs for AHI detection were compared. RESULTS: 7348 persons contributed 9435 specimens and had no evidence of HIV infection, 79 (94 specimens) had established HIV infection, 6 (9 specimens) had pre-seroconversion AHI and 9 (12 specimens) had early seroconversion AHI. Pre-seroconversion AHI cases were not detected by 3rd gen EIA, whereas 2/6 (33.3%) were detected by Siemens 4th gen, 4/6 (66.7%) by Abbott 4th gen and 6/6 (100%) by PNAT. All three EIAs and PNAT detected all individuals with early seroconversion AHI. Overall sensitivity/specificity for the EIAs relative to WB or NAT resolved infection status was 93.6%/99.9% for Siemens 3rd gen, 95.7%/99.7% for Siemens 4th gen and 97.9%/99.2% for Abbott 4th gen. CONCLUSIONS: While both 4th gen EIAs demonstrated improved sensitivity for AHI compared to 3rd gen EIA, PNAT identified more AHI cases than either 4th gen assay. PNAT is likely to remain a useful strategy to identify AHI in high-risk populations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".