Screening program for latent tuberculosis infection in asylum seekers - a single center experience in Pavia, Italy.
Bibliographic record
Abstract
BACKGROUND: The management of Latent Tuberculosis Infection is crucial in fighting Tuberculosis worldwide, and particularly in low incidence European Countries. While guidelines for the management of Tuberculosis in newly arrived immigrants have been issued by the European Center for Disease Control and Prevention and by the National Health Authorities in Italy, these are not widely implemented yet at local level. STUDY DESIGN: We report our program for the screening of Latent Tuberculosis Infection and active Tuberculosis in asylum seekers, jointly implemented by Public Health Authorities and the Infectious Diseases Department of a tertiary care, teaching hospital in Northern Italy. METHODS: We reviewed records of the asylum seekers who were screened at our center via Tuberculin Skin Test and/or Interferon Gamma Release Assay plus chest X-ray and either treated with Isoniazid Preventive Treatment or for active Tuberculosis Disease in case of positive results. RESULTS: We screened 726 migrants, mostly males (97.3%) and from Sub-Saharan Africa (82.2%) and found a high adherence rate for both screening (98.2%) and Isoniazid Preventive Treatment (90.1%). In addition, we found seven cases of active Tuberculosis. CONCLUSIONS: Latent Tuberculosis Infection screening and treatment proved feasible in our program, which should be systematically implemented in asylum seekers reaching Europe.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".