Engaging the Lower Extremity via Active Therapy Early (ELEVATE) Is Feasible and May Improve Gross Motor Function in Children with Spastic Bilateral Cerebral Palsy: A Case Series
Bibliographic record
Abstract
Purpose: The feasibility of ELEVATE with respect to adherence and preliminary efficacy was determined for children with spastic bilateral cerebral palsy (CP) from encephalopathy of prematurity. Methods: A case series was used. Participants were randomized to receive ELEVATE immediately or delay the intervention by 3 months before receiving the intervention. The outcomes included feasibility measures of (1) number of children recruited, (2) percentage of sessions attended, (3) stride counts during the intervention, and preliminary efficacy measures of change over the intervention period in (4) Gross Motor Function Measure-66 (GMFM-66), and (5) kinematics and weight-bearing during treadmill walking. Results: Four boys under 3 years of age participated. All participants tolerated 60-minute intervention sessions four times/week for 12 weeks, and attended 75%-94% (min-max) of the targeted sessions. The median step count per session ranged from 833 to 2484 steps (min-max) during the final week of training. Participants showed an increase in GMFM-66 score of 2.4-7.5 points (min-max) over the 3-month intervention phase, as compared to a decrease of 1.7 for one participant and an increase of 1.3 for another over the delay period. Three participants demonstrated small improvements in their gait with the intervention. Conclusions: Engaging young children with bilateral CP in intensive rehabilitation targeting gross motor function was feasible and demonstrated preliminary efficacy. The results have guided the design of a larger clinical trial to assess efficacy of early, active interventions for children with spastic bilateral CP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".