Use of new resistance markers to predict virologic response to antiretrovirals
Bibliographic record
Abstract
It is relevant that the use of newer more sensitive methods have been key in establishing the role that certain drug-resistance mutations may play in both transmitted and acquired drug resistance. In particular, the use of allele-specific PCR assays (AS-PCR) and pyrosequencing have been very useful in providing new insights. Among the reasons that more ultrasensitive assays may sometimes be needed for more accurate assessments of drug resistance is the differential effect of certain mutations on viral replicative capacity. As an example, the K65R mutation is known to adversely affect HIV replication, and this may be one of the reasons that it is found relatively infrequently among individuals who fail antiretroviral therapy. In contrast, the use of AS-PCR for K65R in subtype C viruses has shown that this method was able to detect the presence of this mutation in an additional 4 of 30 samples who had tested negative by bulk sequencing methods. Now, it also appears as though the transmission of the K65R mutation, while rare, can also be detected in higher numbers by AS-PCR than bulk sequencing, and that this is also more common among subtype C than subtype B viruses. The likely reason is that subtype C viruses are more prone to develop K65R as a consequence of treatment failure and are therefore more likely than subtype B viruses to contain this mutation at the time that transmission takes place. In the case of the M184V mutation, it has also been observed that AS-PCR methods can detect this substitution more efficiently than bulk sequencing among newly-infected individuals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".