Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is a major global health problem that affected an estimated 10 million people worldwide in 2017. The Public Health Agency of Canada monitors active TB disease through a national surveillance system, which is a collaborative effort with the provinces and territories. OBJECTIVE: To present an epidemiological summary of active TB cases reported in 2017. Results are discussed in the context of the previous year's data. Treatment outcomes for cases diagnosed in 2016 are also presented. METHODS: The Canadian Tuberculosis Reporting System 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 the Public Health Agency of Canada and validated by each province and territory. RESULTS: There were 1,796 cases of active TB reported in Canada in 2017 compared with 1,750 cases in 2016, representing a 2.6% increase. There was a corresponding increase in the incidence rate from 4.8 to 4.9 per 100,000 population. Foreign born individuals continued to make up the majority of cases (71.8%) and the incidence rate remained highest among Canadian born Indigenous people (21.5 per 100,000 population), in particular, among the Inuit population (205.8 per 100,000 population). Consistent with the previous decade, TB incidence rates in 2017 continued to be higher among males (5.5 per 100,000) compared with females (4.3 per 100,000), and the majority of cases (45.6%) were between the ages of 15 and 44 years. The incidence rate was highest among adults over 75 years of age (13.8 cases per 100,000 for males and 7.2 for females). Of the TB cases diagnosed in 2016 where outcomes were reported, 80.4% were treated successfully. CONCLUSION: Although the incidence rate of TB in Canada in 2017 remained low in the global context and has been relatively stable over the last decade, both the case count and rate have been gradually increasing since 2014. Indigenous and foreign born Canadians continued to be disproportionately represented among TB cases. Canadian TB surveillance data are an important source of information for monitoring progress and informing public health action related to reducing the burden of TB in Canada, with the ultimate goal of TB elimination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".