Descriptive Review of Tuberculosis Surveillance Systems across Circumpolar Regions.
Bibliographic record
Abstract
INTRODUCTION: Tuberculosis is highly prevalent in many arctic areas. Members of the International Circumpolar Surveillance Tuberculosis (ICS-TB) Working Group collaborate to increase knowledge about tuberculosis in the Arctic region. To allow for the comparison of tuberculosis trends across regions and to establish baseline knowledge on data recording and reporting, ICS-TB reviewed the tuberculosis surveillance systems of member jurisdictions. METHODS: Three questionnaires were developed to reflect the different surveillance levels (local, regional and national); all three were forwarded to the member jurisdictions. The respondent was requested to self-identify the level of surveillance conducted in their region and complete the questionnaire. Information collected included surveillance system objectives, case definitions, data collection methodology, storage and dissemination. RESULTS: Ten ICS-TB jurisdictions [Canada (Labrador, Northwest Territories, Nunavik, Nunavut, Yukon), Greenland, Norway, Sweden, Russian Federation (Khanty-Mansiyskiy Autonomous Okrug), United States, (Alaska)] voluntarily completed the survey; two local, five regional and three national. Tuberculosis reporting is mandatory in all jurisdictions and case definitions are comparable across regions. The main system objectives are to detect outbreaks, and inform the evaluation/planning of public health program and policies. All jurisdictions collect confirmed active tuberculosis cases and treatment outcomes; nine collect contact tracing results. Faxing of standardised case reporting forms is the most common reporting method. Similar core data elements are collected; 7/10 regions report genotyping results. Data are stored using commercial software ( n = 6), customized programs ( n = 3) and one jurisdiction is paper-based. Nine jurisdictions provide monthly, bi-annual or annual reports to principally government and/or scientific/medical audiences.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".