Primary care pearls to help eliminate tuberculosis in Canada
Bibliographic record
Abstract
Although Canada has a low incidence of tuberculosis (TB), certain populations, including the foreign-born and Canadian-born Indigenous peoples, continue to be disproportionately represented among reported cases. The overall incidence rates of active TB in Canada have not significantly changed in the past decade and work still needs to be done to reach TB elimination goals set by the World Health Organization (WHO). In trying to achieve TB elimination in Canada, primary care clinicians, with the support of public health professionals and TB experts, can help by focusing on 1) targeted screening and treatment of latent TB infection (LTBI) and 2) timely diagnosis and referral of active TB disease. The following article focuses on some key primary care considerations to keep in mind in day-to-day patient care. To help conduct targeted screening and treatment for LTBI, several key populations, including immigrants from high TB burden countries, Indigenous peoples and several other at-risk groups, are outlined. Reactivation of LTBI plays a significant role in TB burden and is likely an area of major potential impact in achieving TB elimination. Advancement in LTBI treatment, including short course therapy, is also described. In addition, to help make a timely diagnosis of active TB, several key risk factors, including several co-morbidities which increase the risk of developing TB disease, can be considered. Being front-line in patient care, keeping in mind some of these key pearls may aid primary care providers to have potential impact on eliminating TB in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 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".