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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".