Identifying the Early Post-HIV Antibody Seroconversion Period
Bibliographic record
Abstract
BACKGROUND: Identifying persons with recent human immunodeficiency virus (HIV) antibody seroconversion is useful for treatment, research, and prevention, but the sensitivity and specificity of tests for this purpose are uncertain. METHODS: We used longitudinal specimens panels from 155 persons identified prior to HIV seroconversion to assess antibody-based methods for classifying persons as within 30, 60, or 90 days of seroconversion, including 2 incidence assays, a less-sensitive (LS) enzyme immunoassay (EIA), and the BED assay. RESULTS: Sensitivity and specificity, respectively, for identifying persons within 30 days of seroconversion were: 34%-57% and 98%-100% for 2 standard EIAs (employing a signal-to-cutoff ≤4.0; ≥1.0 defines HIV positive), 84% and 73% for the LS-EIA (≤0.2 cutoff), 88% and 72% for the BED (≤0.2 cutoff), and 43%-58% and 98% (≤3 bands) for 2 Western blot (WB) assays. By area under the receiver operator curves, the best test for identifying persons within 30 days of seroconversion was the number of bands on the Bio-Rad WB (0.90); within 60 days, the LS-EIA and BED (both 0.85); and for persons within 90 days the BED (0.86). CONCLUSIONS: Standard EIAs, Western blots, and HIV incidence assays provide useful information for identifying persons 30 to 90 days after seroconversion.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".