UMOYA: a prospective longitudinal cohort study to evaluate novel diagnostic tools and to assess long-term impact on lung health in South African children with presumptive pulmonary TB—a study protocol
Bibliographic record
Abstract
BACKGROUND: Despite a high paediatric tuberculosis (TB) burden globally, sensitive and specific diagnostic tools are lacking. In addition, no data exist on the impact of pulmonary TB on long-term child lung health in low- and middle-income countries. The prospective observational UMOYA study aims (1) to build a state-of-the-art clinical, radiological, and biological repository of well-characterised children with presumptive pulmonary TB as a platform for future studies to explore new emerging diagnostic tools and biomarkers for early diagnosis and treatment response; and (2) to investigate the short and long-term impact of pulmonary TB on lung health and quality of life in children. METHODS: We will recruit up to 600 children (0-13 years) with presumptive pulmonary TB and 100 healthy controls. Recruitment started in November 2017 and is expected to continue until May 2023. Sputum and non-sputum-based samples are collected at enrolment and during follow-up in TB cases and symptomatic controls. TB treatment is started by routine care services. Intensive follow-up for 6 months will allow for TB cases to retrospectively be classified according to international consensus clinical case definitions for TB. Long-term follow-up, including imaging, comprehensive assessment of lung function and quality of life questionnaires, are done yearly up to 4 years after recruitment. DISCUSSION: The UMOYA study will provide a unique platform to evaluate new emerging diagnostic tools and biomarkers for early diagnosis and treatment response and to investigate long-term outcomes of pulmonary TB and other respiratory events on lung health in children.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".