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 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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.040 | 0.051 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".