Management of Pulmonary Tuberculosis in 2025: An Update for the Respirologist
Bibliographic record
Abstract
Despite being a curable illness, tuberculosis (TB) remains the most common cause of death from an infectious agent, with a reported 10 million infections and 1.25 million TB-related deaths reported globally in 2023. In Canada, the incidence of TB has remained at approximately 5.1 per 100,000 for many years. However, foreign born and Indigenous populations are disproportionately affected by TB, with the Inuit population experiencing the highest TB incidence in Canada, at a reported rate of 70 per 100,000 in 2020.3 Drug-resistant TB is reported in less than 10% of TB isolates in Canada, the majority being isoniazid mono-resistant. Multidrug-resistant TB (MDR-TB), defined as resistance to both isoniazid (INH) and rifampin (RIF), is detected in fewer than 4% of drug-resistant TB isolates in Canada. TB is caused by the Mycobacterium tuberculosis (MTB) bacillus, and is spread when people infected with TB aerosolize the bacteria through coughing, laughing, singing, or talking. Following initial infection, the risk of developing active TB disease is greatest in the first 2 years, then decreases significantly, with an estimated lifetime risk of approximately 10%. In addition to several host factors, the risk of TB reactivation increases in the setting of immune suppression, including HIV infection, malignancy, organ or bone marrow transplant, and immunosuppressive treatments such as prednisone and tumour necrosis factor (TNF)-alpha therapy. Prior to initiating treatments that may increase the risk of reactivation, latent TB infection should be considered and appropriate investigations performed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".