Is Scale-Up Worth It? Challenges in Economic Analysis of Diagnostic Tests for Tuberculosis
Bibliographic record
Abstract
Since 2007, the World Health Organization (WHO) has approved an unprecedented number of new diagnostic tests for tuberculosis (TB) Most recently, Xpert MTB/RIF (Cepheid, Inc.; Sunnyvale, CA), an automated polymerase chain reaction (PCR) test with high accuracy in validation studies (72%-77% sensitivity for smear-negative TB, 99% specificity) To impact TB globally, Xpert MTB/RIF and other diagnostics must be scaled-up across numerous clinical settings, after careful evaluation of expected costs and benefits. Unfortunately, standard cost-effectiveness analyses are ill-suited to guide local decision-makers in directing scale-up activities. We demonstrate the limitations of standard economic analyses as applied to scale-up of TB diagnostics (specifically Xpert MTB/RIF), and recommend adaptations to future analyses that will facilitate rational and effective scale-up activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".