Risk factors for tuberculosis in Greenland: case-control study.
Bibliographic record
Abstract
SETTING AND OBJECTIVE: Despite several efforts aiming at disease control, the incidence of tuberculosis (TB) remains high in Greenland, averaging 131 per 100,000 population during the period 1998-2007. The purpose of the present study was to disclose risk factors for TB. METHODS: A case-control study was performed among 146 patients diagnosed with TB in the period 2004-2006. For each patient, four healthy age- and sex-matched control persons living in the same district were included. All participants completed a questionnaire regarding socio-demographic and lifestyle factors. Risk factor analyses were carried out using logistic regression models. RESULTS: Factors associated with TB were Inuit ethnicity, living in a small settlement, unemployment, no access to tap water, no bathroom or flushing toilet, underweight, smoking, frequent intake of alcohol and immunosuppressive treatment. The multivariate model showed that Inuit ethnicity (OR 15.3), living in a settlement (OR 5.1), being unemployed (OR 4.1) and frequent alcohol use (OR 3.1) were independent determinants of risk. Unemployment was associated with the highest population-attributable risk (29%). CONCLUSION: Risk factors associated with living in a settlement should be further explored and an investigation of genetic susceptibility is warranted.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".