Bibliographic record
Abstract
The treatment of latent tuberculosis infection (LTBI) is an essential component of tuberculosis (TB) elimination in regions that have a low incidence of TB. However, the decision to treat individuals with LTBI must consider the limitations of current diagnostic tests for LTBI, the risk of developing active TB disease, the potential adverse effects from chemoprophylactic therapy, and the importance of treatment adherence. When an individual has been diagnosed with LTBI and active TB has been ruled out, this is followed by an evaluation of the risks and benefits of LTBI treatment within the context of the regional epidemiology of TB and public health priorities. Once the decision to treat LTBI has been reached, and the infection is not suspected to be due to drug-resistant TB, the recommended regimens include isoniazid and/or rifamycin-derivatives, and the choice of regimen will depend upon the clinical considerations for that individual, such as patient preference, concomitant medications, hepatic disease, pregnancy, or immunodeficiency. As the duration of treatment of LTBI therapy is many months, therapy must be offered within a plan that monitors for adverse drug reactions and emphasizes adherence. For latent multidrug-resistant TB (MDR-TB) or extensively drug-resistant TB (XDR-TB) infection, the management is more complicated as there are few options for chemoprophylactic therapy and little evidence regarding the efficacy or risks of these regimens.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".