The Effects of an Early Physical Therapy Intervention for Very Preterm, Very Low Birth Weight Infants: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
PURPOSE: A randomized controlled clinical trial was used to investigate effects of physical therapy (PT) intervention on motor outcome of infants born very preterm with very low birth weight (VLBW). METHODS: Seventy-two infants born very preterm with VLBW were randomly assigned to a nontreatment (NT) (n = 38) or treatment (T) (n = 34) group. The T group received developmental PT from birth until four months corrected age (CA) weekdays during the infant's neonatal stay and on a needs- and problem-orientated basis thereafter. The NT group received no intervention. Both groups were assessed at four months CA using the Alberta Infant Motor Scale (AIMS) as was a control group of 14 infants born full term. Parental compliance was measured using a parent questionnaire. RESULTS: PT intervention had no significant effect on the T group's motor performance. However, no T group subjects had abnormal motor development at four months CA when compared to the NT group (16%) and the control group (14%) (p = 0.09). The T group subjects with high levels of parental compliance had better scores on the AIMS than those with lower parental compliance (p = 0.05). CONCLUSION: PT intervention does not significantly affect motor performance of infants born very preterm with VLBW at four months CA. Parental compliance and intervention frequency may have influenced the outcome. Preliminary evidence suggests that neonatal and early PT may reduce the incidence of motor delay among infants born very preterm with VLBW. Follow-up of this group is recommended to ascertain the long-term benefits of this type of early PT.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".