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
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.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".