Novel and optimized diagnostics for pediatric TB in endemic countries: NOD-pedFEND study protocol
Bibliographic record
Abstract
BACKGROUND: Pediatric tuberculosis is a major global public health challenge, with reliable diagnosis being a main obstacle to identifying and treating affected children. New and improved diagnostics, ideally on non-sputum samples, are urgently required, especially in the most vulnerable group of children under five years of age. Studies to date have been limited by small sample sizes and few bacteriologically-confirmed cases. Here, we describe the study protocol of the NIH-funded NOD-pedFEND study, which will be one of the largest diagnostic studies to date of children at greatest risk of tuberculosis. METHODS: In this prospective observational cohort study, we aim to evaluate existing and novel diagnostic assays, including pathogen- and host-based tests and combinations of tests. A consecutive cohort of children under five years of age with signs and symptoms of tuberculosis is enrolled in Uganda and Peru. All children undergo an extensive baseline workup with signs- and symptoms recording, microbiological reference tests, chest X-ray and tuberculin skin test for rigorous classification according to internationally recognized microbiological, composite reference and strict standards. An array of samples is collected for investigational tests. Follow-up visits are conducted at 2 weeks, 2 months and 6 months. A small cohort of healthy controls is enrolled to evaluate the specificity of selected diagnostics. The study has been approved by the relevant institutional review boards. DISCUSSION: With this large cohort study of children under five years of age, we aim to make an important contribution to the evaluation of new diagnostics for pediatric tuberculosis. By establishing a comprehensive biorepository, the study will also enable the assessment of novel tests as they become available during and after the study.
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How this classification was reachedexpand
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".