Cord blood S100B: reference ranges and interest for early identification of newborns with brain injury
Bibliographic record
Abstract
Background Neurological complications are common in the premature and full-term neonates admitted to the intensive care unit, but the diagnosis of these complications is often difficult to make. S100B protein, measured in cord blood, may represent a valuable tool to better identify patients at risk of brain injury. Methods As a first step, we established S100B cord blood serum reference intervals from 183 preterm and 200 full-term neonates. We then measured cord blood serum S100B to identify neurological complications in 272 neonates hospitalized at the neonatal intensive care unit (NICU). Diagnosis of brain injury relied on imaging examination. Results The 95th percentiles of S100B concentration in cord blood were established as 1.21 μg/L for the 383 neonates, 0.96 μg/L for full-term neonates and 1.36 μg/L for premature neonates. Among the 272 neonates hospitalized at the NICU, 11 presented neurological complications. Using 1.27 μg/L as the optimal sensitivity/specificity threshold, S100B differentiate neonates with and without neurological complications with a sensitivity of 45.5% (95% confidence intervals [CI]: 16.7-76.6) and a specificity of 88.9% (95% CI: 84.4-92.4) (p = 0.006). In combination with arterial pH (<7.25), sensitivity increased to 90.9% (95% CI: 58.7-99.8), while specificity was 51.2% (95% CI: 44.8-57.7). The sensitivity is significantly (p = 0.03) increased in comparison to S100B alone. The specificity is significantly higher with S100B only than with pH + S100B (p < 0.001). Conclusions Cord blood S100B protein, in combination with arterial cord blood pH, has the potential to help clinicians to detect at birth neurological complications in neonates hospitalized in an NCIU.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".