FreezeTB M. tuberculosis drug resistance screening assay v1
Bibliographic record
Abstract
Alaska has the highest incidence of tuberculosis (TB) in the United States with disease disproportionately affecting Alaska Native/American Indian (AN/AI) populations and children under 14 years of age; who account for almost a third of cases. With a case fatality rate of 8% during TB treatment and a quarter of TB cases lacking sputum culture to enable drug resistance testing, the FreezeTB targeted next Generation Sequencing (tNGS) diagnostic initiative was launched to develop diagnostic genotypic drug resistance tools tailored to meet the challenges of care in Alaska, while being translatable to other settings. We sought to develop a rapid and cost-effective method for identifying drug resistant mutations in Mycobacterium tuberculosis using targeted next generation sequencing (tNGS), FREEZE TB: Fast, Reliable, Economical Evaluation tool to Zap Endemic Tuberculosis. We designed a primer multiplex, amplification and barcoding protocol, and sequencing workflow for nanopore sequencers, including portable devices. We programmed an open-source (OS) software that detects mutations associated with drug resistance recognized by the World Health Organization. One hundred blinded M. tuberculosis sputum culture isolates from Alaska (59 resistant, 41 pan-susceptible) were selected and underwent analysis with our the FREEZETB lab and bioinformatic workflow, then compared to phenotypic drug susceptibility testing (pDST) using BACTEC MGIT 960. Compared to WGS (n=79), FreezeTB tNGS and software provided the same mutation report in 96% (n=76/79) of samples. Compared to pDST, FreezeTB had almost perfectvery high (numerator / 79) agreement for RIF (0.904; 71/79) and EMB (1.0; 79/79), substantial agreement for ETO (0.784) and INH (0.795), moderate agreement for STR (0.556) and slight agreement for PZA (0.197) using Cohen’s kappa. FreezeTB is designed to detect resistance to fluoroquinolones, bedaquiline, pretomanid/delamanid, and linezolid;, however, no isolates had thiswere resistant to these drugsce pattern. Using a portable nanopore sequencer, each sample was sequenced for under $30. FreezeTB provides a one-day laboratory and bioinformatic workflow for sequencing M. tuberculosis; offering an OS software with graphical user interface and a laboratory workflow costing under $30 per sample. Freeze TB can accurately determine resistance for rifampin, isoniazid, streptomycin, ethambutol and ethionamide and is designed to detect resistance in newer drugs, such as bedaquiline and pretomanid.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".