Tuberculosis in the Northwest Territories, Canada: A Fourteen-Year Review (2000–13).
Bibliographic record
Abstract
INTRODUCTION: The Northwest Territories (NWT) has one of the highest rates of tuberculosis (TB) in Canada, with a 2011 incidence of 29.8 per 100,000, compared to the Canadian average of 4.7 per 100,000. Furthermore, latent tuberculosis infection (LTBI), a condition in which infected persons do not exhibit evidence of clinically active disease, continues to be a significant concern in this population. The goal of this study is to describe the epidemiology of TB and LTBI in the NWT from 2000 to 2013. METHODS: The study period was 1 January 2000 to 31 December 2013. Patient data were extracted from the Integrated Public Health Information System (iPHIS). Immunization records and cancer statistics were linked using the NWT Immunization Registry and NWT Cancer Registry, respectively. Population data were obtained from the 2011 Canadian Census. All descriptive data analyses were conducted using SPSS and Microsoft Excel. RESULTS: Preliminary descriptive analyses indicate that 136 active cases of TB were diagnosed between 2000 and 2013, giving an average annual incidence rate of 23.4 per 100,000 population. Sixteen cases (12%) were retreatment cases. Respiratory TB was the major disease site, representing 72% of active cases. The majority of active cases were male (63%), and of Aboriginal descent (88%). About a third of cases ( n = 44) were 60 years or older, 10% of cases were aged 0–19 years, and 58% were aged 20–59 years at the time of diagnosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".