Systematic review and meta-analysis of the diagnostic value of four biomarkers in detecting neonatal sepsis in low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Biomarkers may enhance diagnostic capability for common paediatric infections, especially in low- and middle-income countries (LMICs) where standard diagnostic modalities are frequently unavailable, but disease burden is high. A comprehensive understanding of the diagnostic capability of commonly available biomarkers for neonatal sepsis in LMICs is lacking. Our objective was to systematically review evidence on biomarkers to understand their diagnostic performance for neonatal sepsis in LMICs. METHODS: We conducted a systematic review and meta-analysis of studies published in English, Spanish, French, German, Dutch, and Arabic reporting the diagnostic performance of C reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cell count (WBC) and procalcitonin (PCT) for neonatal sepsis. We calculated pooled test characteristics and the area under the curve (AUC) for each biomarker compared with the reference standards blood culture or clinical sepsis defined by each article. RESULTS: Of 6570 studies related to biomarkers in children, 134 met inclusion criteria and included 23 179 neonates. There were 80 (59.7%) studies conducted in LMICs. CRP of ≥60 mg/L (AUC 0.87, 95% CI 0.76 to 0.91) among 1339 neonates and PCT of ≥0.5 ng/mL (AUC 0.87, 95% CI 0.70 to 0.92) among 617 neonates demonstrated the greatest discriminatory value for the diagnosis of neonatal sepsis using blood culture as the reference standard in LMICs. CONCLUSIONS: PCT and CRP had good discriminatory value for neonatal sepsis in LMICs. ESR and WBC demonstrated poor discrimination for neonatal sepsis in LMICs. Future studies may incorporate biomarkers into clinical evaluation in LMICs to diagnose neonatal sepsis more accurately. PROSPERO REGISTRATION NUMBER: CRD42020188680.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".