Baseline Clinical HIV Genotypes Are a Valid Measure of Transmitted Drug Resistance Within the Treatment-Naive Population
Bibliographic record
Abstract
OBJECTIVE: To examine whether baseline clinical genotypes are equivalent to diagnostic serum genotypes for surveillance of HIV transmitted drug resistance (TDR). DESIGN: Current HIV TDR surveillance in Canada is conducted through genotyping remnant diagnostic sera from new HIV diagnoses. As part of routine care, baseline genotyping is now conducted on all newly diagnosed HIV infections, with TDR data being generated a second time on the same patients. METHODS: Surveillance genotyping, on HIV diagnostic serum, was performed on newly diagnosed HIV cases from 2007 to 2010 in Alberta, Canada. All subjects with a baseline clinical genotype result on file, and no evidence of antiretroviral therapy, were studied further. The HIV sequences from diagnosis and from the first clinical genotype were compared according to elapsed time between testing and by evaluating timing of infection based on BED capture enzyme immunoassay (BED-CEIA, abbreviated as BED in this article). RESULTS: Eighty-seven genotype pairs were available for analysis, most of which were subtype B. The time between genotypes ranged from 0 to 755 days, with a median of 36 days and an interquartile range of 155.25 days. Genetic distance between genotypes varied between 0 and 0.03389 substitutions per site and did not correlate with sampling times. There was a tendency for the genotypes of infections classified as recent by BED to be more similar to their clinical genotypes but this effect was lost when adjusted for elapsed time between tests. There was no difference in the identified drug resistance. CONCLUSIONS: Baseline clinical genotypes from treatment-naive patients may be used for HIV TDR surveillance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".