A systematic review of the costs of diagnosis for multidrug-resistant/extensively drug-resistant TB in different settings
Bibliographic record
Abstract
BACKGROUND: We performed an analysis of the cost and relative merits of different strategies for the diagnosis of multidrug-resistant/extensively drug-resistant TB (MDR/XDR-TB) in different settings.METHODS: We systematically reviewed the published evidence on cost/cost-effectiveness of rapid MDR/pre-XDR-TB and other methods for XDR-TB testing up to September 2022. PRISMA guidelines were followed. Collected data were analysed using Stata v17 software. Cost data were reported in USD ($) and summarised by mean, standard deviation, and range. Country income level was defined according to the World Bank country classification. Three simplified scenarios were also used to explore testing implications, based on low, intermediate and high TB incidence.RESULTS: Of 157 records, 25 studies were included with 24 reporting the cost of Xpert/RIF and two that evaluated the implementation of the MTBDRplus test. The total rapid test cost ranged from $12.41–$218, including $1.13–$74.60 for reagents/consumables and $0.40–$14.34 for equipment.CONCLUSION: The cost of MDR/XDR-TB diagnostics is lower in low resource settings. However, the cost-effective implementation of MDR/XDR-TB diagnostic algorithms requires careful consideration of local resources to avoid missed identification and the use of inappropriate regimen.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".