Real-Time Sequencing of Mycobacterium tuberculosis: Are We There Yet?
Bibliographic record
Abstract
ABSTRACT Whole-genome sequencing has taken a leading role in epidemiologic studies of tuberculosis, but thus far, its real-time clinical utility has been low, in part because of the requirement for culture. In their report in this issue, Votintseva et al. (A. A. Votintseva, P. Bradley, L. Pankhurst, C. del Ojo Elias, M. Loose, K. Nilgiriwala, A. Chatterjee, E. G. Smith, N. Sanderson, T. M. Walker, M. R. Morgan, D. H. Wyllie, A. S. Walker, T. E. A. Peto, D. W. Crook, and Z. Iqbal, J Clin Microbiol 55:1285–1298, 2017, https://doi.org/10.1128/JCM.02483-16 ) present a new method for extracting Mycobacterium tuberculosis DNA directly from smear-positive respiratory samples, making it feasible to generate drug resistance predictions and phylogenetic trees in 44 h with the Illumina MiSeq. They also illustrate the potential for a <24-h turnaround time from DNA extraction to clinically relevant results with Illumina MiniSeq and Oxford Nanopore Technologies MinION. We comment on the promise and limitations of these approaches.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".