Pediatric tuberculosis immigration screening in high-immigration, low-incidence countries.
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) screening in migrant children, including immigrants, refugees and asylum seekers, is an ongoing challenge in low TB incidence countries. Many children from high TB incidence countries harbor latent TB infection (LTBI), and some have active TB disease at the point of immigration into host nations. Young children who harbor LTBI have a high risk of progression to TB disease and are at a higher risk than adults of developing disseminated severe forms of TB with significant morbidity and mortality. Many countries have developed immigration TB screening programs to suit the needs of adults, but have not focused much attention on migrant children. OBJECTIVE: To compare the TB immigration medical examination requirements in children in selected countries with high immigration and low TB incidence rates. DESIGN: Descriptive study of TB immigration screening programs for systematically selected countries. RESULTS: Of 18 eligible countries, 16 responded to the written survey and telephone interview. CONCLUSION: No two countries had the same approach to TB screening among migrant children. The optimal evidenced-based manner in which to screen migrant children requires further research.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".