Diagnostic Accuracy of Point-of-Care Testing of C-Reactive Protein, Interleukin-6, and Procalcitonin in Neonates with Clinically Suspected Sepsis: A Prospective Observational Study
Bibliographic record
Abstract
OBJECTIVE: Sepsis often prompts clinicians to start empirical antibiotics in suspected neonates while awaiting diagnosis. The next-generation testing with point-of-care (POC) techniques offers a lead-time advantage that could bridge the gap by providing a timely diagnosis. MATERIALS AND METHODS: We conducted a prospective diagnostic study in 82 neonates enrolled between May and October 2022 in a level III neonatal intensive care unit. All neonates with a new episode of clinically suspected sepsis were included. Diagnostic accuracy of POC testing of C-reactive protein (CRP), interleukin-6 (IL-6), and procalcitonin (PCT) with standard laboratory methods was performed. RESULTS: The mean gestation age and birth weight of the neonates were 33.17 ± 4.25 weeks and 1,695.4 ± 700.74 grams, respectively. Most neonates were preterm (75%) with nearly equal proportions of early (51.22%) and late-onset (48.78%) sepsis. The POC CRP correlated well with standard CRP (r = 0.8001, 95% CI: 0.706-0.867, p < 0.0001). Among the three biomarkers, CRP had the maximum diagnostic accuracy (area under the curve [AUC] - 0.73) followed by PCT (AUC - 0.65) and IL-6 (0.55). There was no significant difference in the diagnostic accuracy of CRP (p = 0.46), PCT (p = 0.29), and IL-6 (p = 0.60) in early- and late-onset sepsis. The mean time for POC estimation of IL-6, PCT, and CRP was 12 ± 3 min which was significantly less compared to 366 ± 61 min for standard techniques (p < 0.001). CONCLUSION: POC CRP correlates well with standard techniques of estimation, and CRP alone and in combination with PCT has good diagnostic accuracy in neonatal sepsis.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".