Genetic Diversity as a Marker for Timing Infection in HIV-Infected Patients: Evaluation of a 6-Month Window and Comparison With BED
Bibliographic record
Abstract
BACKGROUND: It has been reported that the increase in human immunodeficiency virus (HIV) sequence diversity in drug resistance surveillance specimens may be used to classify the duration of HIV infection as <1 or >1 year. We describe a mixed base classifier (MBC) optimized to categorize the duration of subtype B infections as <6 or >6 months on the basis of sequences for drug resistance surveillance specimens and compared MBC findings with those of serologic methods. METHODS: The behavior of the MBC was examined across a range of thresholds for calling mixed bases. MBC performance was then evaluated using either complete pol sequences or sites reflecting evolutionary pressures (HLA selection sites, sites that increased in entropy over the course of infection, and codon positions). RESULTS: The MBC performance was optimal when secondary peaks on the sequencing chromatogram accounted for at least 15% of the area of primary peaks. A cutoff of <0.45% mixed bases in the pol region best identified recent infections (sensitivity = 82.7%, specificity = 78.8%), with improvement achieved by analyzing only sites that increased in entropy. CONCLUSIONS: In an extended data set of 1354 specimens classified by BED, the optimized MBC performed significantly better than a simple MBC (agreement, 68.98% vs 67.13%). If further validated, the MBC may prove beneficial for detecting recent infection and estimating the incidence of HIV infection.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".