Reply to Waters et al.
Bibliographic record
Abstract
To the Editor—We agree that HIV reverse-transcriptase (RT) codon 245 polymorphisms should not be used to deny abacavir therapy to patients [1]. As has been emphasized, the positive predictive value of the codon 245 test is low [1, 2]; ideally, persons with codon 245 polymorphisms should be screened for HLA-B*5701 prior to abacavir use. Instead, the high negative predictive value of the codon 245 test indicates that persons with wild-type RT 245V are at a substantially reduced risk for the abacavir hypersensitivity reaction. Given the ready availability of HIV RT sequence data (collected during routine drug-resistance testing) examining virus variation at codon 245 could be useful in situations in which HLA testing is limited or cost-prohibitive. HLA-B*5701 allele prevalence is typically highest among white individuals (∼5%) [3]. However, the prevalence of HLA-B*5701 in the population tested by Waters et al. [1] was ∼10-fold higher (54.7%), which is unprecedented in any unselected population. Waters et al. [1] specifically selected HLA-B*5701-positive individuals, with a random control group of HLA-B*5701-negative individuals for comparison (A. Pozniak, personal communication). Because negative predictive value depends on the characteristics of the population tested [4], the negative predictive value calculated by Waters et al. [1] is accurate only for this highly selected population and is irrelevant for the general population of HIV-infected individuals receiving antiviral drugs. The 2 large, prospective cohorts from which the study subset used in Waters et al. [1] was drawn exhibit a typical HLA-B*5701 allele prevalence of ∼7% [5, 6]. With this HLA-B*5701 prevalence, the negative predictive value in their population, using their reported 92.5% sensitivity, would actually be 99%, broadly similar to that given in our original report [2], and with overlapping confidence intervals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".