sierra-local: A lightweight standalone application for drug resistance prediction
Bibliographic record
Abstract
Genotypic resistance interpretation systems for the prediction and interpretation of HIV-1 antiretroviral resistance are an important part of the clinical management of HIV-1 infection.Current interpretation systems are generally hosted on remote webservers that enable clinical laboratories to generate resistance predictions easily and quickly from patient HIV-1 sequences encoding the primary targets of modern antiretroviral therapy.However they also potentially compromise a health provider's ethical, professional, and legal obligations to data security, patient information confidentiality, and data provenance.Furthermore, reliance on web-based algorithms makes the clinical management of HIV-1 dependent on a network connection.Here, we describe the development and validation of sierra-local, an open-source implementation of the Stanford HIVdb genotypic resistance interpretation system for local execution, which aims to resolve the ethical, legal, and infrastructure issues associated with remote computing.This package reproduces the HIV-1 resistance scoring by the web-based Stanford HIVdb algorithm with a high degree of concordance (99.997%) and a higher level of performance than current methods of accessing HIVdb programmatically.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".