Treatment of latent infection to achieve tuberculosis elimination in low-incidence countries
Bibliographic record
Abstract
points• Tuberculosis (TB) persists in the United States, Canada, and other high-income, lowincidence countries largely because of ongoing reactivation of latent TB infection (LTBI).• TB elimination in low-incidence countries, defined as an annual incidence of �1 case per million, will require extensive screening and treatment of LTBI, including in people for whom the harms of LTBI treatment outweigh the likely benefits: for example, older foreign-born individuals with no recent travel/exposure.Ongoing migration from higher-incidence countries, as well as pockets of transmission in vulnerable subgroups such as prisoners, homeless persons, and drug users, will also continue to pose challenges for TB elimination.• Policymakers in low-incidence countries face a choice between a utilitarian approach that tolerates individual net harm to advance public health goals and a patient-centered approach that values shared decision-making but will predictably result in failure to achieve TB elimination.• While TB elimination is an important aspirational vision, the ethical implications of this goal-namely the implicit requirement to offer LTBI screening and treatment to individuals who are more likely to experience harm than good-merit careful reflection.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".