Treating latent tuberculosis with rifampin: is it the cheaper option?
Bibliographic record
Abstract
Treatment of latent tuberculosis infection (LTBI) is an important measure for tuberculosis control in the developed world. A recent study estimated that between 291 000 and 433 000 persons started LTBI treatment in the USA in 2002 and that between 4000 and 11 000 cases of active tuberculosis were prevented by this treatment.1 However, many persons for whom LTBI treatment is recommended fail to initiate or complete treatment. Another recent cross-sectional survey of clinics in the USA and Canada showed that fewer than 50% of persons prescribed LTBI treatment completed the prescribed course.2 Key barriers to successful completion of treatment include the length and suboptimal tolerability of the 9-month course of isoniazid that is most frequently used for LTBI treatment. Shorter better-tolerated regimens are clearly needed. The study by Aspler et al 3 in this issue of Thorax ( see page 582 ) examines one such regimen—namely, 4 months of daily rifampin. This regimen, while recommended as an alternative option for LTBI treatment by the Centers for Disease Control and Prevention,4 has not been widely adopted in the USA. Major barriers to adoption include the possibility of inadvertent treatment of active tuberculosis with rifampin resulting in rifampin-monoresistant disease, concerns about effectiveness and the increased cost of rifampin compared with isoniazid. Aspler et al addressed the latter concern …
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".