Modeling the Impact of Alternative Strategies for Rapid Molecular Diagnosis of Tuberculosis in Southeast Asia
Bibliographic record
Abstract
Novel diagnostic tests hold promise for improving tuberculosis (TB) control, but their epidemiologic impact remains uncertain. Using data from the World Health Organization (2011-2012), we developed a transmission model to evaluate the deployment of 3 hypothetical TB diagnostic tests in Southeast Asia under idealized scenarios of implementation. We defined diagnostics by their sensitivity for smear-negative TB and proportion of patients testing positive who initiate therapy ("point-of-care amenability"), with tests of increasing point-of-care amenability having lower sensitivity. Implemented in the public sector (35% of care-seeking attempts), each novel test reduced TB incidence by 7%-9% (95% uncertainty range: 4%-13%) and mortality by 20%-22% (95% uncertainty range: 14%-27%) after 10 years. If also deployed in the private sector (65% of attempts), these tests reduced incidence by 13%-16%, whereas a perfect test (100% sensitivity and treatment initiation) reduced incidence by 20%. Annually detecting 20% of prevalent TB cases through targeted screening (70% smear-negative sensitivity, 85% treatment initiation) also reduced incidence by 19%. Sensitivity and point-of-care amenability are equally important considerations when developing novel diagnostic tests for TB. Novel diagnostics can substantially reduce TB incidence and mortality in Southeast Asia but are unlikely to transform TB control unless they are deployed actively and in the private sector.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".