Fluoroquinolone Therapy for the Prevention of Multidrug-Resistant Tuberculosis in Contacts: A Cost-Effectiveness Analysis
Bibliographic record
Abstract
RATIONALE: Fluoroquinolone (FQN) therapy of latent tuberculosis infection among contacts of individuals with multidrug-resistant tuberculosis (MDR-TB) is controversial. OBJECTIVES: To determine the potential benefits, risks (including acquired FQN resistance), and cost-effectiveness of FQN therapy to prevent TB in contacts of individuals with MDR-TB. METHODS: We used decision analysis to estimate costs and outcomes associated with no therapy compared with a 6-month course of daily FQN therapy to treat latent TB infection in contacts of individuals with MDR-TB. Outcomes modeled were the incidence of MDR-TB, MDR-TB with FQN resistance, TB-related death, quality-adjusted life years, and health system costs. MEASUREMENTS AND MAIN RESULTS: FQN preventive therapy resulted in health system savings, lower incidence of MDR-TB, and lower mortality than no treatment. We found the incidence of MDR-TB with acquired FQN resistance would also be lower with FQN therapy of infected contacts. CONCLUSIONS: In our model, FQN preventive therapy resulted in substantial health system savings and in reduced mortality, incidence of MDR-TB, and incidence of acquired FQN-resistant disease as well as improved quality of life. FQN therapy remained cost saving with improved outcomes even if the effectiveness of therapy in preventing MDR-TB was as low as 10%.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".