A Microbiological Revolution Meets an Ancient Disease: Improving the Management of Tuberculosis with Genomics
Bibliographic record
Abstract
Tuberculosis (TB) is an ancient disease with an enormous global impact. Despite declining global incidence, the diagnosis, phenotyping, and epidemiological investigation of TB require significant clinical microbiology laboratory resources. Current methods for the detection and characterization of Mycobacterium tuberculosis consist of a series of laboratory tests varying in speed and performance, each of which yields incremental information about the disease. Since the sequencing of the first M. tuberculosis genome in 1998, genomic tools have aided in the diagnosis, treatment, and control of TB. Here we summarize genomics-based methods that are positioned to be introduced in the modern clinical TB laboratory, and we highlight how recent advances in genomics will improve the detection of antibiotic resistance-conferring mutations and the understanding of M. tuberculosis transmission dynamics and epidemiology. We imagine the future TB clinic as one that relies heavily on genomic interrogation of the M. tuberculosis isolate, allowing for more rapid diagnosis of TB and real-time monitoring of outbreak emergence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".