Laboratory Guidelines for the Practical Use of HIV Drug Resistance Tests in Patient Follow-Up
Bibliographic record
Abstract
HIV drug resistance is one of the major limitations in the successful treatment of HIV-infected patients using currently available antiretroviral combination therapies. When appropriate, drug susceptibility profiles should be taken into consideration in the choice of a specific combination therapy. Guidelines recommending resistance testing in certain circumstances have been issued. Many clinicians have access to resistance testing and will increasingly use these results in their treatment decisions. In this document, we comment on the different methods available, and the relevant issues relating to the clinical application of these tests. Specifically, the following recommendations can be made: (i) genotypic and phenotypic HIV-1 drug resistance analyses can yield complementary information for the clinician. However, insufficient information currently exists as to which approach is preferable in any particular clinical setting; (ii) when HIV-1 drug resistance testing is required, it is recommended that testing be performed on plasma samples obtained before starting, stopping or changing therapy, on samples that have a viral load above the detection limit of the resistance test; (iii) the panel recommends that genotypic and phenotypic HIV-1 drug resistance testing for clinical purposes be performed in a certified laboratory under strict quality control and quality assurance standards; and (iv) the panel recommends that resistance testing laboratories provide clinicians with resistance reports that include a list of drug-related resistance mutations (genotype) and/or a list of drug-related fold resistance values (phenotype), with interpretations of each by an experienced virologist. The interpretation of genotypic and phenotypic analysis is a complex and developing science, and in order to understand HIV-1 drug resistance reports, communication between the requesting clinician and the expert that interpreted the resistance report is recommended.
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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 | 0.034 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.040 |
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".