Risk of tuberculosis in screened subjects without known risk factors for active disease.
Bibliographic record
Abstract
SETTING: Tuberculosis (TB) referral clinic in Vancouver, British Columbia, Canada. BACKGROUND: Screening for and treatment of latent TB infection (LTBI) in at-risk populations are the cornerstone of TB control in low-incidence countries. Persons at low risk often undergo the tuberculin skin test (TST) for reasons other than contact. Little information exists on the actual risk of TB in this population. OBJECTIVE: To determine the risk of TB in screened subjects without known risk factors. DESIGN: Retrospective descriptive analysis of demographics, TST reaction size and TB disease occurrence in 98333 low-risk subjects screened from 1990 to 2002. RESULTS: The average annual disease rate was 0.4 per 100000 population (cumulative rate 7.4/100000) from 1990 to 2006, and TB was diagnosed only in the foreign-born. Risk of TB in the foreign-born increased with larger TST reaction size (P < 0.03). Completion of treatment for LTBI was not documented for any of the subsequent active TB cases. CONCLUSION: In a low-risk screened population, active TB disease was found only in the foreign-born. Treatment of LTBI is not recommended in persons with a positive TST and no additional risk factors. Local screening programs should focus on populations with confirmed risk factors for disease.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".