Acute Pain Assessment in Prematurely Born Infants Below 29 Weeks
Bibliographic record
Abstract
OBJECTIVES: Neonates born extremely prematurely are at high risk of acute and prolonged pain. Effective treatment requires reliable pain assessment, which is currently missing. Our study explored whether existing pain assessment tools and physiological indicators measure pain and comfort accurately in this population. MATERIALS AND METHODS: We prospectively collected data in 16 neonates born at less than 29 weeks' gestational age during 3 conditions: skin-to-skin care, rest, and heelstick procedure for capillary blood sampling in the incubator. The neonates were video recorded in these situations, and recordings were coded using 5 observational pain assessment tools and numeric rating scales for pain and distress. We simultaneously collected heart rate, respiratory rate, arterial oxygen saturation, regional cerebral oxygenation, and the number of skin conductance peaks. All measures across the 3 conditions were compared using general linear modeling. RESULTS: The median gestational age was 27.1 weeks (range: 24.1 to 28.7). Forty measurement periods across the 3 conditions were analyzed. Heart rate was significantly higher during heelstick procedures compared with during rest, with a mean difference of 10.7 beats/min (95% confidence interval [CI]: 2.7-18.6). Oxygen saturation was significantly higher during skin-to-skin care compared with during heelstick procedures with a mean difference of 5.5% (95% CI: 0.2-10.8). The Premature Infant Pain Profile-revised (PIPP-R) score was significantly higher during heelstick procedures compared with skin-to-skin care with a mean difference of 3.2 points (95% CI: 1.6-5.0). DISCUSSION: Pain measurement in clinical practice in prematurely born infants below 29 weeks remains challenging. The included behavioral and physiological indicators did not adequately distinguish between a painful situation, rest, and skin-to-skin care in premature neonates.
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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 | 0.005 |
| 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".