Coverage and yield of entry and follow-up screening for tuberculosis among new immigrants
Bibliographic record
Abstract
The aim of the present study was to determine the effectiveness of entry screening for tuberculosis and biannual follow-up screening among new immigrants in The Netherlands. To achieve this, the present authors analysed screening, prevalence and incidence data of 68,122 immigrants, who were followed for 29 months. Patients diagnosed within 5 months and 6-29 months after entry screening were considered to be detected at entry and during the follow-up period, respectively. Coverage of the second to fifth screening rounds was 59, 46, 36 and 34%, respectively. Yield of entry screening was 119 per 100,000 individuals, and prevalence at entry was 131 per 100,000. Average yield of follow-up screening was highest among immigrants with abnormalities on chest radiography (CXR) at entry (902 per 100,000 individuals). When excluding these, yield of follow-up screening was 9, 37 and 97 per 100,000 screenings for immigrants from countries with tuberculosis incidences of <100, 100-200 and >200 per 100,000, respectively. The incidence during follow-up in individuals with a normal CXR was 11, 58 and 145 per 100,000 person-yrs follow-up in these groups. The proportion of cases detected through screening declined per screening round from 91 to 31%. Yield of entry screening was high. Overall coverage and yield of follow-up screening was low. Follow-up screening of immigrants with a normal chest radiograph from countries with an incidence of <200 per 100,000 individuals was therefore discontinued.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".