Current Treatment Options for Latent Tuberculosis Infection
Bibliographic record
Abstract
Treatment of latent tuberculosis infection (LTBI) is a key component in TB control strategies worldwide. However, as people with LTBI are neither symptomatic nor contagious, any screening decision should be weighed carefully against the potential benefit of preventing active disease in those who are known to be at higher risk and are willing to accept therapy for LTBI. This means that a targeted approach is desirable to maximize cost effectiveness and to guarantee patient adherence. We focus on LTBI treatment strategies in patient populations at increased risk of developing active TB, including candidates for treatment with tumor necrosis factor-α blockers. In the last 40 years, isoniazid (INH) has represented the keystone of LTBI therapy across the world. Although INH remains the first therapeutic option, alternative treatments that are effective and associated with increased adherence and economic savings are available. Current recommendations, toxicity, compliance, and cost issues are discussed in detail in this review. A balanced relationship between the patient and healthcare provider could increase adherence, while cost-saving treatment strategies with higher effectiveness, fewer side effects, and of shorter duration should be offered as preferred.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".