Near-Infrared Spectroscopic Screening for Bladder Disease in Africa: Training Rural Clinic Staff to Collect Data of Diagnostic Quality
Bibliographic record
Abstract
Background. While near-infrared spectroscopy (NIRS) has recognized relevance for developing countries, biomedical applications are rare. This reflects the cost and complexity of NIRS and the convention of comprehensive training for accurate data collection. In an international initiative using transcutaneous NIRS to screen for bladder disease in Africa, we evaluated if interactive training enabled clinic staff to collect data accurately.Methods. Workshop training in a Ugandan medical clinic on NIRS monitoring theory; bladder physiology and chromophore changes occurring with disease; device orientation; device positioning over the bladder, monitoring subjects during voiding; and saving/uploading data. Participation in patient screening followed with observation, assistance, and then data collection. Evaluation comprised conduct of serial independent screenings with analysis if saved files were of diagnostic quality.Results. 10 individuals attended 1-hour workshops and then 0.5–3.0 hours of screening. Five then felt able to conduct screening independently and all collected data were of diagnostic quality (>5 consecutive patients); all had participated in screening for >1.5 hours (6+ subjects); less participation allowed competent assistance but not consistent adherence to the monitoring protocol.Conclusion. A simplified NIRS system, small-group theory/orientation workshops, and >I.5 hours of 1 : 1 training during screening enabled clinic staff in Africa to collect accurate NIRS data.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".