Historical review of studies on the effect of treating latent tuberculosis
Bibliographic record
Abstract
Tuberculosis Preventive Therapy (TPT) is widely used in particular among high-risk populations such as close contacts and immunosuppressed people mostly in high-income settings. TPT is widely recommended for high-risk populations including HIV-infected and household contacts globally, but is not widely used. Historical trials on risk groups as well as the general population have documented a marked effect on reductions in incidence of active disease among those treated, as well as on prevalence of latent TB infection (LTBI) in populations where massive roll-out of TPT has previously taken place. This review summarizes the results of large historical trials conducted more than 50 years ago among Inuit and African populations as well as risk groups in the USA and Europe exhibiting similarities with current high-burden populations with current limited use of TPT. The trials demonstrated a 27-95% reduction in incidence of active TB among those receiving preventive treatment compared with placebo, with efficacy depending somewhat on length of treatment but mostly on adherence rates. It was possible to achieve satisfactory adherence rates in most of the trial populations and liver toxicity rates were generally low. The historical trials on preventive treatment for LTBI have documented that large-scale TPT is possible and effective even in high-burden populations in high-incidence areas and is therefore a relevant tool to consider in striving to eliminate the TB epidemic.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".