Fewer losses in the cascade of care for latent tuberculosis with solo interferon-gamma release assay screening compared to sequential screening
Bibliographic record
Abstract
BACKGROUND: Refugees are at increased risk of developing tuberculosis (TB) soon after resettlement. Targeting high-risk populations for latent tuberculosis infection (LTBI) screening and treatment is an important measure towards eliminating TB in low incidence countries, however, there are low rates of screening and treatment completion in the LTBI cascade of care. The authors hypothesized that an interferon-gamma release assay (IGRA) screening strategy would lead to a higher proportion of refugees completing LTBI screening and treatment, compared to sequential screening with tuberculin skin test (TST) and confirmatory IGRA. METHODS: This retrospective cohort study included eligible refugees screened with a sequential strategy versus a solo-IGRA strategy at different time periods from a centralized refugee clinic. The primary outcome was the proportion completing LTBI screening in each cohort. RESULTS: A total of 471 subjects were included (240 in sequential screening, 231 in solo-IGRA screening). 54% of refugees completed LTBI screening with sequential testing, compared to 85% of those screened with a solo-IGRA. Time to completing screening was also shorter in the solo-QFT group (difference 16.5 days, p < 0.01, 95% confidence interval 9.3, 23.7). There was a higher incidence of LTBI diagnosis in the solo-IGRA group (41 versus 20, p = 0.002). Screening completion was predicted by solo-IGRA screening (aOR 3.74, 95% confidence interval 2.30, 6.09; p < 0.001) and if refugees were privately-sponsored (aOR 2.81, 95% confidence interval 1.53, 5.15; p = 0.001). Treatment completion rates did not differ between groups. CONCLUSION: This study has identified fewer dropouts in the LTBI cascade of care if a solo-IGRA strategy is used for screening. An IGRA should be strongly considered as the screening method for refugees arriving in low-incidence settings if resources are available.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".