Tuberculosis in the Circumpolar Region, 2006-2012
Bibliographic record
Abstract
Setting The northern circumpolar jurisdictions Canada (Northwest Territories, Nunavik, Nunavut, Yukon), Finland, Greenland, Norway, Russian Federation (Arkhangelsk), Sweden and the United States (Alaska). Objective To describe and compare demographic, clinical and laboratory characteristics, including drug resistance and treatment completion, of tuberculosis (TB) cases in the northern circumpolar populations. Design Descriptive analysis of all active TB cases reported from 2006 to 2012 for incidence rate (IR), age and sex distribution, sputum smear and diagnostic site characteristics, drug resistance and treatment completion rates. Results The annual IR of TB disease ranged from a low of 4.3 per 100 000 population in Northern Sweden to a high of 199.5/100 000 in Nunavik, QC, Canada. For all jurisdictions, IR was higher for males than for females. Yukon had the highest proportion of new cases compared with retreatment cases (96.6%). Alaska reported the highest percentage of laboratory-confirmed cases (87.4%). Smear-positive pulmonary cases ranged from 25.8% to 65.2%. Multidrug-resistant cases ranged from 0% (Northern Canada) to 46.3% (Arkhangelsk). Treatment outcome data, available up to 2011, demonstrated >80% treatment completion for four of the 10 jurisdictions. Conclusion TB remains a serious public health issue in the circumpolar regions. Surveillance data contribute toward a better understanding and improved control of TB in the north.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".