Does breastfeeding reduce acute procedural pain in preterm infants in the neonatal intensive care unit? A randomized clinical trial
Bibliographic record
Abstract
Managing acute procedural pain effectively in preterm infants in the neonatal intensive care unit remains a significant problem. The objectives of this study were to evaluate the efficacy of breastfeeding for reducing pain and to determine if breastfeeding skills were altered after this treatment. Fifty-seven infants born at 30-36 weeks gestational age were randomized to be breastfed (BF) or to be given a soother during blood collection. Changes in the Behavioral Indicators of Infant Pain (BIIP) and in mean heart rate (HR) across 3 phases of blood collection were measured. In the BF group, the Premature Infant Breastfeeding Behaviors (PIBBS) scale was scored before and 24 hours after blood collection. Longitudinal regression analysis was used to compare changes in Lance/squeeze and Recovery phases of blood collection between groups, with gestational age at birth, baseline BIIP scores, and mean HR included as covariates. Differences in PIBBS scores were assessed using a paired t-test. Relationships between PIBBS scores, BIIP scores, and HR were evaluated with Pearson correlations. No differences between treatment groups were found: BIIP (P=0.44, confidence interval [CI] -1.60-0.69); HR (P=0.73, CI -7.0-10.0). Infants in the BF group showed improved PIBBS scores after the treatment (P<0.01, CI -2.7 to -0.2). Lower BIIP scores during the Lance/squeeze were associated significantly with more mature sucking patterns (r=-0.39, P<0.05). Breastfeeding during blood collection did not reduce pain indices or interfere with the acquisition of breastfeeding skills. Exploratory analyses indicate there may be benefit for infants with mature breastfeeding abilities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".