Hair Cortisol as a Potential Biologic Marker of Chronic Stress in Hospitalized Neonates
Bibliographic record
Abstract
BACKGROUND: As preterm and term infants in the neonatal intensive care unit (NICU) undergo multiple stressful/painful procedures, research is required that addresses chronic stress. OBJECTIVES: To determine whether (a) hair cortisol levels differed between term and preterm infants exposed to stress in the NICU and (b) an association exists between hair cortisol levels and severity of illness or indicators of acute stress. METHODS: Hair cortisol levels were determined using the ELISA method (solid-phase enzyme-linked immunoassay, Alpco Diagnostics, Windham, N.H., USA) in 60 infants >25 weeks gestational age at birth. RESULTS: No significant differences were found between the hair cortisol levels of term infants compared to preterm infants in the NICU. When compared to a group of healthy term infants, hospitalized infants had significantly higher hair cortisol levels (t (76) = 2.755, p = 0.004). A subgroup analysis of the term NICU infants showed a statistically significant association between total number of ventilator days and hair cortisol levels. For every extra day on the ventilator, hair cortisol levels increased on average by 0.2 nmol/g (p = 0.03). 21% of the variance in hair cortisol levels was explained by the total number of days on the ventilator. CONCLUSIONS: Hair cortisol is influenced by days of ventilation in NICU term infants. This is a potentially valid outcome for chronic neonatal stress in these infants and warrants further investigation.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".