Procalcitonin in preterm infants during the first few days of life: introducing an age related nomogram
Bibliographic record
Abstract
OBJECTIVE: To determine normal concentrations of procalcitonin in preterm infants shortly after birth and to assess its accuracy in detecting bacterial infection. METHODS: Blood samples of 100 preterm infants were prospectively drawn during the first 4 days of life for determination of procalcitonin concentration. Infants were classified into four groups according to their sepsis status. RESULTS: Mean (SD) gestational age and birth weight were 32 (2.9) weeks and 1682 (500) g respectively. A total of 283 procalcitonin concentrations from healthy infants were plotted to construct nomograms of physiologically raised procalcitonin concentration after birth, stratified by two groups to 24-30 and 31-36 weeks gestation. The peak 95th centile procalcitonin concentration was plotted at 28 hours of age; values return to normal after 4 days of life. Only 12 infants were infected, and 13 of their 16 procalcitonin concentrations after birth were higher than the 95th centile, whereas samples taken at birth were lower. In a multivariable analysis, gestational age, premature rupture of membrane, and sepsis status influenced procalcitonin concentration independently, but maternal infection status did not. CONCLUSIONS: The suggested neonatal nomograms of preterm infants are different from those of term infants. Procalcitonin concentrations exceeding the 95th centile may be helpful in detecting congenital infection, but not at birth.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".