The implications of whole-genome sequencing in the control of tuberculosis
Bibliographic record
Abstract
The availability of whole-genome sequencing (WGS) as a tool for the diagnosis and clinical management of tuberculosis (TB) offers considerable promise in the fight against this stubborn epidemic. However, like other new technologies, the best application of WGS remains to be determined, for both conceptual and technical reasons. In this review, we consider the potential value of WGS in the clinical laboratory for the detection of Mycobacterium tuberculosis and the prediction of antibiotic resistance. We also discuss issues pertaining to data generation, interpretation and dissemination, given that WGS has to date been generally performed in research labs where results are not necessarily packaged in a clinician-friendly format. Although WGS is far more accessible now than it was in the past, the transition from a research tool to study TB into a clinical test to manage this disease may require further fine-tuning. Improvements will likely come through iterative efforts that involve both the laboratories ready to move TB into the genomic era and the front-line clinical/public health staff who will be interpreting the results to inform management decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".