Negative latent tuberculosis at time of incarceration: identifying a very high-risk group for infection
Bibliographic record
Abstract
The main aim was to measure the incidence of latent tuberculosis infection (LTBI) and identify risk factors associated with infection. In addition, we determined the number needed to screen (NNS) to identify LTBI and active tuberculosis. We followed 129 prisoners for 2 years following a negative two-step tuberculin skin test (TST). The cumulative incidence of TST conversion over 2 years was 29·5% (38/129), among the new TST converters, nine developed active TB. Among persons with no evidence of LTBI, the NNS to identify a LTBI case was 3·4 and an active TB case was 14·3. The adjusted risk factors for LTBI conversion were incarceration in prison number 1, being formerly incarcerated, and overweight. In conclusion, prisoners have higher risk of LTBI acquisition compared with high-risk groups, such as HIV-infected individuals and children for whom LTBI testing should be performed according to World Health Organization guidance. The high conversion rate is associated with high incidence of active TB disease, and therefore we recommend mandatory LTBI screening at the time of prison entry. Individuals with a negative TST at the time of entry to prison are at high risk of acquiring infection, and should therefore be followed in order to detect convertors and offer LTBI treatment. This approach has a very low NNS for each identified case, and it can be utilized to decrease development of active TB disease and transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".