Systematic Review and Meta-Analysis of Diagnostic Biomarkers for Pediatric Pneumonia
Bibliographic record
Abstract
BACKGROUND: Pneumonia causes significant morbidity and mortality in children worldwide, especially in resource-poor settings. Accurate identification of bacterial etiology leads to timely antibiotic initiation, minimizing overuse, and development of resistance. Host biomarkers may improve diagnostic sensitivity and specificity. We assessed the ability of biomarkers to correctly identify bacterial pneumonia in children who present with respiratory distress. METHODS: A librarian-directed search was conducted of MEDLINE, EMBASE, CENTRAL, Global Health, the World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov to May 2020 with no language restriction. Included studies compared a diagnostic biomarker in children with bacterial pneumonia to those with nonbacterial respiratory distress. RESULTS: There were 31 observational studies of 23 different biomarkers. C-reactive protein (CRP), procalcitonin (PCT), white blood cell (WBC) count, and erythrocyte sedimentation rate (ESR) were the biomarkers with sufficient data for meta-analysis. Meta-analysis revealed that CRP and PCT best differentiated bacterial from viral pneumonia with CRP summary AUROC (area under the receiver operating characteristic curve) 0.71 (0.69-0.73), Youden index 53 mg/L, sensitivity 0.70 (0.68-0.78), and specificity 0.64 (0.58-0.68) and PCT summary AUROC 0.70 (0.67-0.74), Youden index 0.59 ng/mL, sensitivity 0.69 (0.65-0.77), and specificity 0.64 (0.60-0.68). WBC and ESR did not perform as well. Nineteen other inflammatory and immunologic biomarkers were identified including CRP/mean platelet value, neutrophil/leukocyte ratio, interleukin 6, and interferon-alpha, with sensitivities from 60% to 85% and specificities from 76% to 83%. CONCLUSION: CRP and PCT performed better than WBC and ESR but had suboptimal sensitivity. Some less well-studied novel biomarkers appear to have promise particularly in combination.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".