Protective Efficacy of BCG Vaccine against Mycobacterium leprae and Non-Tuberculous Mycobacterial Infections
Bibliographic record
Abstract
The genus mycobacterium includes several species that are known to cause infections in humans. The microorganisms are classified into tuberculous and non-tuberculous based on their morphological characteristics, defined by the dynamic relationship between the host defenses and the infectious agent. Non-tuberculous mycobacteria (NTM) include all the species of mycobacterium other than the ones that cause tuberculosis (TB). The group of NTM contains almost 200 different species and they are found in soil, water, animals—both domestic and wild—milk and food products, and from plumbed water resources such as sewers and showerhead sprays. A systematic review of Medline between 1946 and 2014 showed an 81% decline in TB incidence rates with a simultaneous 94% increase in infections caused by NTM. Prevalence of infections due to NTM has increased relative to infections caused by TB owing to the stringent prevention and control programs in Western countries such as the USA and Canada. While the spread of typical mycobacterial infections such as TB and leprosy involves human contact, NTM seem to spread easily from the environment without the risk of acquiring from a human contact except in the case of M. abscessus in patients with cystic fibrosis, where human transmission as well as transmission through fomites and aerosols has been recorded. NTM are opportunistic in their infectious processes, making immunocompromised individuals such as those with other systemic infections such as HIV, immunodeficiencies, pulmonary disease, or usage of medications such as long-term corticosteroids/TNF-α inhibitors more susceptible. This review provides insight on pathogenesis, treatment, and BCG vaccine efficacy against M. leprae and some important NTM infections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".