Analysis of genotypic and phenotypic clinical cut‐off levels for ritonavir‐boosted saquinavir
Bibliographic record
Abstract
There is a need for new, clinically relevant interpretation algorithms for genotypic and phenotypic resistance for ritonavir-boosted saquinavir (SQV/r) at the current approved dosage [1000/100 mg twice a day (bid)]. Clinical cut-off levels, which correlate baseline measures of resistance with HIV RNA responses in large cohorts or clinical trials, are the ideal reference for developing such algorithms. Cut-off levels previously developed for unboosted saquinavir may no longer apply, as the plasma drug levels with SQV/r are significantly higher and may be able partially to overcome protease inhibitor-resistant HIV. Clinical cut-off levels for SQV/r, assessed in several cohort studies and clinical trials, also suggest that multiple genotypic mutations are required for complete loss of virological response. For phenotypic analysis of resistance, saquinavir cut-off levels 10-11-fold higher than the wild-type IC50 have best distinguished responders from non-responders in cohort studies. Using Virtual Phenotype, a 12.3-fold upper cut-off level was determined from analysis of large cohort databases. These genotypic and phenotypic algorithms need to be validated in larger prospective studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".