Active case‐finding in contacts of people with TB
Bibliographic record
Abstract
BACKGROUND: Exposure to people with TB substantially elevates a person's risk of tuberculous infection and TB disease. Systematic screening of TB contacts enables the early detection and treatment of co‐prevalent disease, and the opportunity to prevent future TB disease. However, scale‐up of contact investigation in high TB transmission settings remains limited.METHODS: We undertook a narrative review to evaluate the evidence for contact investigation and identify strategies that TB programmes may consider when introducing contact investigation and management.RESULTS: Selection of contacts for priority screening depends upon their proximity and duration of exposure, along with their susceptibility to develop TB. Screening algorithms can be tailored to the target population, the availability of diagnostic tests and preventive therapy, and healthcare worker expertise. Contact investigation may be performed in the household or at communal locations. Local contact investigation policies should support vulnerable patients, and ensure that drop‐out during screening can be mitigated. Ethical issues should be anticipated and addressed in each setting.CONCLUSION: Contact investigation is an important strategy for TB elimination. While its epidemiological impact will be greatest in lower‐transmission settings, the early detection and prevention of TB have important benefits for contacts and their communities.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".