Bibliographic record
Abstract
TO THE EDITOR—Abbate and colleagues [1] report similar findings to ours [2] in terms of the utility of deep sequencing for the determination of human immunodeficiency virus (HIV) tropism, lending further confidence to this approach. They note that the major advantage of our results is the evaluation of the ability of deep sequencing to predict actual virologic outcomes to CCR5-antagonist medication rather than its comparison with a nominal Trofile assay call. Abbate and colleagues [1] rightfully express caution at extending our results (which were generated using HIV RNA from plasma) to the peripheral blood mononuclear cell (PBMC) compartment from which cell-associated HIV DNA may be amplified and tested. Specifically, they caution against using the same cutoff point of 2% non-R5 variants used for plasma samples in our study [2] to apply to PBMC samples, given that they and others have reported higher X4 prevalence, higher variability, and unclear clinical relevance for this compartment [3–5]. We believe that the need for clinical validation of the PBMC compartment is not exclusive to genotypic tropism testing, but also applies to phenotypic assays that start with cellular HIV DNA.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.031 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".