Tuberculosis in Greenland — current situation and future challenges
Bibliographic record
Abstract
OBJECTIVE: To describe the tuberculosis (TB) epidemiology in Greenland in 1998-2002 and to identify possible obstacles for reducing the TB incidence. STUDY DESIGN/METHODS: TB notification data were collected from the annual reports of the Chief Medical Officer, and culture verification data were collected from the International Reference Laboratory of Mycobacteriology at Statens Serum Institut, Denmark. RESULTS: The TB incidence in Greenland reached a peak of 185/100,000 in 2001. In 1999-2001, the majority of cases were related to an outbreak in the Southern districts. In 1998-2002, 0.5% drug-resistance was found among patients living in Greenland in contrast to 13.1% drug-resistance found previously among Inuit patients in Denmark. In 1998-2001, microscopy positive cases made up 65% of all culture confirmed cases and DNA subtyping demonstrated the emergence of Mycobacterium tuberculosis strains that were previously infrequently found. CONCLUSION: It is important to eliminate factors that fuel the epidemic and to improve general living conditions in Greenland. Treatment seems effective as limited drug-resistance is detected. TB reduction will therefore depend on early detection of active disease and thorough contact tracing. Greenland will face a pool of persons latently infected some of whom will progress to active disease. Sufficient resources need to be allocated for TB control in the years to come.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".