Cost-effectiveness of newer technologies for the diagnosis of Mycobacterium tuberculosis infection in Brazilian people living with HIV
Bibliographic record
Abstract
Tuberculosis is the leading cause of death among people living with HIV (PLH). Preventive tuberculosis therapy reduces mortality in PLH, especially in those with a positive tuberculin skin test (TST). New, more specific technologies for detecting latent tuberculosis infection (LTBI) are now commercially available. We sought to analyse the cost-effectiveness of four different strategies for the diagnosis of LTBI in PLH in Brazil, from the Brazilian public health care system perspective. We developed a Markov state-transition model comparing four strategies for the diagnosis of LTBI over 20 years. The strategies consisted of TST with the currently used protein purified derivative (PPD RT 23), two novel skin tests using recombinant allergens (Diaskintest [Generium Pharmaceutical, Moscow, Russia] and EC [Zhifei Longcom Biologic Pharmacy Co., Anhui, China]), and the QuantiFERON-TB-Gold-Plus (Qiagen, Hilden, Germany). The main outcome was cost (in 2020 US dollars) per quality-adjusted life years (QALY). For the base case scenario, the Diaskintest was dominant over all other examined strategies. The cost saving estimate per QALY was US $1375. In sensitivity analyses, the Diaskintest and other newer tests remained cost-saving compared to TST. For PLH, TST could be replaced by more specific tests in Brazil, considering the current national recommendations.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".