Zoonotic tuberculosis in India: looking beyond <i>Mycobacterium bovis</i>
Bibliographic record
Abstract
Abstract Background Zoonotic tuberculosis (zTB) is the transmission of Mycobacterium tuberculosis complex (MTBC) subspecies from animals to humans. zTB is generally quantified by determining the proportion of human isolates that are Mycobacterium bovis . Although India has the world’s largest number of human TB cases and the largest cattle population, where bovine TB is endemic, the burden of zTB is unknown. Methods To obtain estimates of zTB in India, a PCR-based approach was applied to sub-speciate positive MGIT® cultures from 940 patients (548 pulmonary, 392 extrapulmonary disease) at a large referral hospital in India. Twenty-five isolates of interest were subject to whole genome sequencing (WGS) and compared with 715 publicly available MTBC sequences from South Asia. Findings A conclusive identification was obtained for 939 samples; wildtype M. bovis was not identified (95% CI: 0 – 0.4%). There were 912 M. tuberculosis sensu stricto (97.0%, 95% CI: 95.7 – 98.0), 7 M. orygis (95% CI: 0.3 – 1.5%); 5 M. bovis BCG, and 15 non-tuberculous mycobacteria. WGS analysis of 715 MTBC sequences again identified no M. bovis (95% CI: 0 – 0.4%). Human and cattle MTBC isolates were interspersed within the M. orgyis and M. tuberculosis sensu stricto lineages. Interpretation M. bovis prevalence in humans is an inadequate proxy of zTB in India. The recovery of M. orygis from humans, together with the finding of M. tuberculosis in cattle, underscores the need for One Health investigations to assess the burden of zTB in countries with endemic bovine TB. Funding Bill & Melinda Gates Foundation, Canadian Institutes for Health Research
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".