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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it