Prediction of early-onset neonatal sepsis in umbilical cord blood analysis: an integrative review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe the inflammatory markers studied in umbilical cord blood and to analyze the performance of the three markers most frequently studied for the prediction of early-onset neonatal sepsis. DATA SOURCES: An integrative review from 1995 to 2021 was performed, with a search in the MEDLINE, Embase, Cochrane Library, SciELO, and gray literature databases, using the terms "neonates," "newborns," "neonatal sepsis," "early-onset neonatal sepsis," "neonatal infection," "inflammatory markers," "biomarkers," "cord blood," "fetal blood." STUDY SELECTION AND DATA EXTRACTIONS: Study evaluation was limited to primary studies, prospective, observational or intervention, descriptive or analytical, that assessed the diagnosis of early-onset neonatal sepsis using inflammatory markers in umbilical cord blood, in Portuguese, English, or Spanish. Qualitative studies, reports, review studies, and case series were excluded. Only studies with a punctuation ≥ 6 in the Newcastle-Ottawa scale were included. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: Sixteen studies were included in the qualitative synthesis. Procalcitonin, C-reactive protein, and interleukin-6 were the most frequently studied markers. The best performance for C-reactive protein was observed at a 0.2 mg/L cutoff, with a sensitivity of 82% and a negative predictive value of 99%. Procalcitonin presented the best performance at a 0.5 ng/mL cutoff with 87.5% sensitivity and 98.7% negative predictive value. Interleukin-6 presented the best performance at a 108.5 ng/mL cutoff, with 95% sensitivity and 97.4% negative predictive value. CONCLUSION: The evaluation of markers in the umbilical cord for the diagnosis of early-onset neonatal sepsis, could contribute to a more assertive therapy for the neonate and anticipate sepsis screening. Since the cost is less and technically easier, C-reactive protein is recommended for routine use.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".