Neonatal pain in relation to postnatal growth in infants born very preterm
Bibliographic record
Abstract
Procedural pain is associated with poorer neurodevelopment in infants born very preterm (≤ 32 weeks gestational age), however, the etiology is unclear. Animal studies have demonstrated that early environmental stress leads to slower postnatal growth; however, it is unknown whether neonatal pain-related stress affects postnatal growth in infants born very preterm. The aim of this study was to examine whether greater neonatal pain (number of skin-breaking procedures adjusted for medical confounders) is related to decreased postnatal growth (weight and head circumference [HC] percentiles) early in life and at term-equivalent age in infants born very preterm. Participants were n=78 preterm infants born ≤ 32 weeks gestational age, followed prospectively since birth. Infants were weighed and HC measured at birth, early in life (median: 32 weeks [interquartile range 30.7-33.6]) and at term-equivalent age (40 weeks [interquartile range 38.6-42.6]). Weight and HC percentiles were computed from sex-specific British Columbia population-based data. Greater neonatal pain predicted lower body weight (Wald χ(2)=7.36, P=0.01) and HC (Wald χ(2)=4.36, P=0.04) percentiles at 32 weeks postconceptional age, after adjusting for birth weight percentile and postnatal risk factors of illness severity, duration of mechanical ventilation, infection, and morphine and corticosteroid exposure. However, later neonatal infection predicted lower weight percentile at term (Wald χ(2)=5.09, P=0.02). Infants born very preterm undergo repetitive procedural pain during a period of physiological immaturity that appears to impact postnatal growth, and may activate a downstream cascade of stress signaling that affects later growth in the neonatal intensive care unit.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".