Impact of massage therapy on motor outcomes in very low‐birthweight infants: Randomized controlled pilot study
Bibliographic record
Abstract
BACKGROUND: The purpose of the present study was to determine the effects of massage therapy on motor development, weight gain, and hospital discharge in preterm very low-birthweight (VLBW) newborns. METHODS: Twenty-four preterm VLBW newborns (<34 weeks and <1500 g) were enrolled in this randomized controlled pilot study. The intervention group (n = 12) received massage therapy starting at 34 weeks post-conceptional age (15 min daily, 5 days/week for 4 weeks). The infants in the sham treatment group (n = 12) received similar duration of light still touch. Test of Infant Motor Performance (TIMP) score gain, weight gain, and post-conceptional age at discharge were compared between the two groups after intervention using Mann-Whitney U-test. RESULTS: No significant between-group difference in TIMP score gain and weight gain was identified when all subjects were analyzed. In subgroup analysis, among those with below-average pre-treatment TIMP score (<35), the intervention group (n = 6) achieved significantly higher TIMP score gain (P = 0.043) and earlier hospital discharge (P = 0.045) than the sham treatment group (n = 5). These same infants, however, also had significantly shorter duration of total parenteral nutrition than their counterparts in the sham treatment group (P = 0.044). CONCLUSIONS: Massage therapy might be a viable intervention to promote motor outcomes in a subgroup of VLBW newborns with poor motor performance. A larger randomized controlled trial is required to further explore the effects of massage therapy in this high-risk group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".