Bibliographic record
Abstract
OBJECTIVE: To review clinical aspects of management of tuberculosis (TB) infection and disease in Canadian children in the context of the global TB epidemic and the rising incidence of drug-resistant TB. DATA SOURCES: ORIGINAL AND REVIEW ARTICLES PERTINENT TO: epidemiology of TB globally and in Canada; management of latent TB infection and TB disease in children; diagnostic tests for latent TB infection and TB disease; and management of drug-resistant TB disease. Multiple Medline searches were used including combinations of the MeSH terms 'Tuberculosis*' (and its multiple subheadings), 'Child*', 'Drug Resistance', 'Mycobacterium tuberculosis*' and 'Canada/epidemiology*'. Select relevant textbooks were reviewed. DATA SELECTION AND EXTRACTION: The articles were analyzed from the perspective of clinicians managing children in Canada today, and from our experience of managing children with TB in Southern Ontario. DATA SYNTHESIS: TB in Canada is largely a disease of the foreign-born and their children, but continues to occur in aboriginal children. Drug resistance is increasing globally and in Canada. Most children with TB disease in Canada are asymptomatic and found through contact tracing. False positive skin tests are frequent where TB prevalence is low. CONCLUSIONS: Obtain source case drug sensitivities when treating TB contacts and those with latent TB infection. Obtain cultures before treating TB disease and treat disease with at least four antituberculous drugs while awaiting sensitivities. Use Directly Observed Therapy for TB disease. Confine TB skin testing to children at high risk for TB infection or disease, including contacts of infectious patients and recent immigrants. A team approach and infection control measures including environmental controls are important in managing TB disease.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".