Wellness on Wheels (WoW): Iterative evaluation and refinement of mobile computer-assisted chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria
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
Abstract Background: Wellness on Wheels (WoW) is a model of mobile systematic TB screening of high-risk populations combining digital chest radiography with computer-aided automated interpretation and chronic cough screening to identify presumptive TB in communities, health facilities and prisons in Nigeria. Understanding how models are designed and refined over time helps others to anticipate technical and political challenges, replicate successful strategies, and avoid common mistakes. Methods: We piloted and refined approaches in phased evaluations, recalibrating CAD4TB thresholds to balance TB yield and feasibility. Iterative data monitoring of screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Risk factors for loss along the diagnostic cascade were identified and mitigation plans were implemented. Participants with high likelihood on CAD4TB (≥80) who tested negative on a single spot GeneXpert were followed-up. Results: Gradual improvements included: achieving screening targets (64.0% to 70.5%), risk group inclusion (91.5% to 92.9%), on-site sample processing (84.3% to 86.1%), treatment initiation (86.7% to 90.8%), treatment success (70.6% to 83.2%), and NNT (8.2 to 7.6). However, expectoration by asymptomatic presumptive participants (≈85%) and HIV testing coverage (64.9%) remained suboptimal. Conclusion : Mobile computer-assisted digital chest x-ray and chronic cough screening with GeneXpert MTB/RIF testing is feasible, acceptable, efficient and high-yield when highest risk groups and key stakeholders are engaged, and operations evolve in real time to fix problems. CAD4TB scores should be used to identify people who need clinical diagnosis and/or longer-term follow-up for progression to TB disease.
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 | 0.010 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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