[Tuberculosis screening of foreigners in European, North-American, and Oceanian countries].
Bibliographic record
Abstract
PURPOSE: To plan a tuberculosis control program of foreign-born people in Japan, we reviewed the policies of tuberculosis screening on entrance for immigrants and non-immigrant visitors other than refugees and asylum-seekers in European, North American and Oceanic countries. METHODS: Medical literature review and Internet search for the official governmental web sites. RESULTS: In most countries, the main targets of tuberculosis screening programs for foreign-born people are refugees and asylum-seekers. Very few countries have a tuberculosis screening system on entrance for non-immigrant visitors. Such counties include Norway, The Netherlands, UK, Canada, New Zealand and Australia. The USA only screens immigrants who will settle permanently in USA. Screening policies and methods are highly variable, but many of the screening systems are not working well. The effectiveness of mass screening on entrance by chest X-ray, as a tuberculosis control program, is not well analyzed, and the validity of such screening is questionable. CONCLUSION: It is not accurate to think that a tuberculosis-screening program for foreign-born people on entrance to a country is an effective world standard. We must adopt a wider perspective in planning a tuberculosis control program for foreign-born people, including community-based approaches.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".