Biomarkers for diagnosis of childhood tuberculosis: A systematic review
Bibliographic record
Abstract
INTRODUCTION: As studies of biomarkers of tuberculosis (TB) disease provide hope for a simple, point-of-care test, we aimed to synthesize evidence on biomarkers for diagnosis of TB in children and compare their accuracy to published target product profiles (TPP). METHODS: We conducted a systematic review of biomarkers for diagnosis of pulmonary TB in exclusively paediatric populations, defined as age less than 15 years. PubMed, EMBASE and Web of Science were searched for relevant publications from January 1, 2000 to November 27, 2017. Studies using mixed adult and paediatric populations or reporting biomarkers for extrapulmonary TB were excluded. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) framework. No meta-analysis was done because the published childhood TB biomarkers studies were mostly early stage studies and highly heterogeneous. RESULTS: The 29 studies included in this systematic review comprise 20 case-control studies, six cohort studies and three cross-sectional studies. These studies reported diverse and heterogeneous forms of biomarkers requiring different types of clinical specimen and laboratory assays. Majority of the studies (27/29 [93%]) either did not meet the criteria in at least one of the four domains of the QUADAS-2 reporting framework or the assessment was unclear. However, the diagnostic performance of biomarkers reported in 22 studies met one or both of the WHO-recommended minimal targets of 66% sensitivity and 98% specificity for a new diagnostic test for TB disease in children, and/or 90% sensitivity and 70% specificity for a triage test. CONCLUSION: We found that majority of the biomarkers for diagnosis of TB in children are promising but will need further refining and optimization to improve their performances. As new data are emerging, stronger emphasis should be placed on improving the design, quality and general reporting of future studies investigating TB biomarkers 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".