Neonatal Invasive Procedures Predict Pain Intensity at School Age in Children Born Very Preterm
Bibliographic record
Abstract
INTRODUCTION: Children born very preterm display altered pain thresholds. Little is known about the neonatal clinical and psychosocial factors associated with their later pain perception. OBJECTIVE: We aimed to examine whether the number of neonatal invasive procedures, adjusted for other clinical and psychosocial factors, was associated with self-ratings of pain during a blood collection procedure at school age in children born very preterm. MATERIALS AND METHODS: 56 children born very preterm (24 to 32 weeks gestational age), followed longitudinally from birth, and free of major neurodevelopmental impairments underwent a blood collection by venipuncture at age 7.5 years. The children's pain was self-reported using the Coloured Analog Scale and the Facial Affective Scale. Parents completed the Child Behavior Checklist and the State-Trait Anxiety Inventory. Pain exposure (the number of invasive procedures) and clinical factors from birth to term-equivalent age were obtained prospectively. Multiple linear regression was used to predict children's pain self-ratings from neonatal pain exposure after adjusting for neonatal clinical and concurrent psychosocial factors. RESULTS: A greater number of neonatal invasive procedures and higher parent trait-anxiety were associated with higher pain intensity ratings during venipuncture at age 7.5 years. Fewer surgeries and lower concurrent child externalizing behaviors were associated with a higher pain intensity. CONCLUSIONS: In very preterm children, exposure to neonatal pain was related to altered pain self-ratings at school age, independent of other neonatal factors. Neonatal surgeries and concurrent psychosocial factors were also associated with pain ratings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".