Accuracy, Precision, and Consistency of Expert HIV Type 1 Genotype Interpretation: An International Comparison (The GUESS Study)
Bibliographic record
Abstract
BACKGROUND: Resistance testing is considered standard of care in HIV medicine, but there is no standard interpretation system for genotype tests. We sought to determine how much agreement exists within a group of experts in the interpretation of complex genotypes. METHODS: Genotypes from clinical specimens were sent to an international panel of 12 resistance experts. Phenotypic susceptibility testing of these clinical isolates was performed with antivirogram. Experts predicted phenotype fold change category (<2.5-fold change, 2.5-4.0-fold change, >4.0- to 7.0-fold change, >7.0- to 10-fold change, >10- to 20-fold change, or >20-fold change) and predicted expected drug activity for each of 16 antiretroviral drugs. Experts were also asked to make treatment recommendations on the basis of the genotype. RESULTS: The experts predicted the exact phenotype fold change category correctly 44% of the time, but they varied widely by antiretroviral drug (range, 25%-74%). The highest accuracy was observed for lamivudine (74%) and the nonnucleoside reverse transcriptase inhibitors (66%-69%). Experts generally predicted higher levels of resistance to the remaining nucleoside reverse transcriptase inhibitors than what was found by phenotypic testing. Agreement among experts in predicting phenotype fold change category ranged widely depending on the drug (median agreement, 42% [range, 28%-74%]); the same pattern was observed in predicting expected drug activity (median agreement, 45% [range, 32%-87%]). Experts agreed on treatment recommendations in a median of 79% of instances, and recommendations were consistent over time, with blinded retesting. CONCLUSIONS: Although their ability to predict phenotype from a genotype varied for individual antiretroviral drugs, this expert panel had a high degree of agreement in deriving treatment recommendations from the genotype.
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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 | 0.046 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".