Is Scale-Up Worth It? Challenges in Economic Analysis of Diagnostic Tests for Tuberculosis
Bibliographic record
Abstract
Background: Scaling Up Rapid Diagnostic TestsSince 2007, the World Health Organization (WHO) has approved an unprecedented number of new diagnostic tests for tuberculosis (TB) [1,2].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) [3,4], was endorsed by WHO [5] and reduced in price [6].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.The Essay section contains opinion pieces on topics of broad interest to a general medical audience.
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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 | 0.112 | 0.286 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".