Exploring the epidemiological impact of universal access to rapid tuberculosis diagnosis using agent-based simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many high-burden countries have committed to providing universal access to rapid diagnosis of tuberculosis (TB), but the corresponding impact on population-wide incidence is unknown. We designed an agent-based simulation of drug-susceptible (DS) and drug-resistant (DR) TB in a representative Indian setting and compared the impact of Xpert testing via a decentralized (Xpert available at each local-population) versus centralized (Xpert available at the district-level serving multiple local-populations) strategy. Decentralized testing resulted in a 36% reduction in DR-TB incidence at 10 years compared to no Xpert. Depending on assumptions regarding pre-treatment loss to follow-up (ranging from 5 to 50%), the impact of centralized testing ranged from a 35% to 22% reduction in DR-TB incidence. Implementation of Xpert by either approach had a negligible impact (<5%) on DS-TB incidence. Decisions regarding choice of centralized vs. decentralized Xpert will heavily depend on operational aspects of centralized Xpert and loss to follow-up.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it