Are Twitches, Startles, and Body Movements Pain Indicators in Extremely Low Birth Weight Infants?
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to examine whether body activity such as postural, trunk, and limb movements may be potential pain cues in preterm infants. DESIGN: Convenience sample. SETTING: Level III neonatal intensive care unit (NICU). PATIENTS: Extremely low birth weight (< or = 1,000 g) preterm infants (n = 64) undergoing routine NICU medical care. OUTCOME MEASURES: Procedures likely to differ in evoking distress (i.e., endotracheal suctioning, chest physical therapy, diaper change, or nasogastric feed) were observed. Behaviors were recorded at bedside using the Neonatal Individualized Developmental Care and Assessment Program system. RESULTS: Changes in heart rate and sleep/waking state were related to the procedures, supporting the assumption of differing relative disruption to the infant. Arching, squirming, startles, and twitching were not observed significantly more during procedures than at baseline. After controlling for background variables, finger splay and leg extension were significantly related to ongoing procedures. Facial brow raising was a function of the number of invasive procedures in the past 24 hours; thus, it may be a useful cue of sensitization. CONCLUSIONS: Some extensor movements seemed to be distress signals, whereas tremors, startles, and twitches were not related to discomfort during the observation period. These behaviors may differ qualitatively during longer lasting tissue invasive events. The results of this study indicate the need for more in-depth study of patterns of motor activity in preterm infants over longer observation periods to evaluate potential signs of stress and pain in babies undergoing NICU medical care.
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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 | 0.000 |
| 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".