Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is a major global health problem that affected an estimated 10.4 million people worldwide in 2016. The Public Health Agency of Canada (PHAC) monitors active TB disease through a national surveillance system, which is a collaborative effort withthe provinces and territories. OBJECTIVE: This article presents an epidemiological summary of the active TB disease cases reported from 2006 to 2016, with a focus on 2016. Treatment outcomes for cases diagnosed in 2015 are also presented. METHODS: The Canadian Tuberculosis Reporting System (CTBRS) is a case-based surveillance system that maintains non-nominal data on people diagnosed with active TB disease in Canada. Data are collected annually from the provinces and territories, analyzed by PHAC and validated by each province and territory. RESULTS: The number of active TB disease cases increased from 1,642 in 2015 to 1,737 in 2016, corresponding to an increase in incidence rate from 4.6 to 4.8 per 100,000 population. Foreign born individuals continued to make up the majority of cases reported (70%) and the incidence rate remained highest among Canadian born Indigenous people (23.5 per 100,000 population) and was particularly high within the Inuit population (170.1 per 100,000 population). Over the past decade, there was a slight decrease in the number of cases among children and the proportion of re-treatment cases declined from 8.3% of cases in 2006 to 5.4% of cases in 2016. CONCLUSION: Although tuberculosis incidence rates in Canada are low in the global context and have been relatively stable over the last decade, there has been a slight increase in rates over the last three years, especially in the foreign born population which accounts for the majority of cases. The decrease in cases among children suggests less active transmission and the low proportion of re-treatment cases suggests effective treatment and adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".