Reply to “Issues on Results of the External Quality Assessment for Proviral DNA Testing of HIV-1 Tropism in the Maraviroc Switch Collaborative Study”
Bibliographic record
Abstract
We appreciate the comments by Berg et al. regarding our article. First, the aim of the quality assessment program reported in this study was not to standardize the methodology across the network of laboratories involved but to assess the outcome of laboratories testing clinical material using their laboratory’s in-house standard protocol for determination of HIV-1 tropism using proviral DNA. As MARCH is an international study, the different HIV subtype distribution necessitated having nonstandardized methodologies and, hence, use of different primers required to amplify different clades. We agree that samples had a high degree of variability, with some samples having a very wide range in their false-positive rates (FPRs), and these were solid findings. Our external quality assessment (EQA) samples were derived from real patient samples and deliberately selected to be similar to those recruited to MARCH, i.e., long-term aviremic patients. The use of only clones or other homogeneous samples would therefore not be a true representation of the genetic diversity of real patient samples and would have led to a misrepresentation of the performance of laboratories testing such samples for diagnostics. One could have separately evaluated the two different issues, diversity of samples and diversity of laboratory performance. However, we presented the data in one single examination to assess the overall impact of such material as an international quality assessment program. Furthermore, it was interesting that Berg et al. compared our study to that of Svicher et al. This study used samples from viremic patients (10,000 copies/ml) and tested HIV RNA, which is more likely to deliver uniform results because the circulating virus is likely to reflect the fittest clone/quasispecies circulating in that patient at that time. The DNA in long-term aviremic patients is likely to be far more heterogeneous, reflecting multiple quasispecies of archived virus of varying fitness. We felt very strongly that a clonal panel would not be helpful for the purposes of our program. The observed variability validates the importance of performing this EQA, and these data will be critical for interpreting the results of the MARCH study itself. We acknowledge that triplicate testing is costly and not feasible in all laboratory settings. Nevertheless, we felt triplicate testing was essential, considering both the inherent variability of proviral DNA and the lack of information from rigorously conducted randomized clinical trials, to the clinical relevance of this test. Therefore, we consider it far more important to detect X4 viruses and be conservative in terms of patient safety. Overall, and for the reasons described both in the article and in this response, we do not believe that singlicate testing of a clonal standard would reliably ensure that a patient’s virus would likely respond to the protocol drug, maraviroc. In summary, we feel that Berg et al. have misinterpreted the intent of the EQA program established specifically for MARCH. Proviral DNA, obtained from real patient samples, was associated with a high degree of variability, and the most suitable approach for quality assessment for determination of viral tropism was developed for this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.043 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".