Goal-Directed Personalized Upper Limb Intensive Therapy (PULIT) for Children With Hemiparesis: A Retrospective Analysis
Bibliographic record
Abstract
IMPORTANCE: Children with hemiparesis experience limitations in activities of daily living (ADLs) as a result of upper limb impairments. To address these limitations, we developed a group-based Personalized Upper Limb Intensive Therapy (PULIT) program combining modified constraint-induced movement therapy, bimanual intensive therapy, and exergame-based robotics. OBJECTIVE: To determine the effectiveness of PULIT in helping children with upper limb impairments achieve individually set goals and enable transfer of the attained motor skills into ADLs. DESIGN: Retrospective analysis. SETTING: Day camp at a pediatric rehabilitation clinic in Switzerland. PARTICIPANTS: Twenty-three children with upper limb impairment (unilateral cerebral palsy, n = 16; acquired brain injury, n = 7); 13 boys and 10 girls (M age = 7 yr, 8 mo, SD = 2 yr, 1 mo; Manual Ability Classification System Level I-IV). INTERVENTION: Thirty hours of PULIT over the course of 8 days. OUTCOMES AND MEASURES: Goal attainment scaling (GAS) was assessed on the first and last day of intervention. The Canadian Occupational Performance Measure (COPM) and dexterity tests, such as the Box and Block Test (BBT), were administered 3 wk before and 3 wk after the intervention. RESULTS: Total goal achievement was 85.7%. GAS, parent- and child-rated COPM Performance and Satisfaction, and the BBT of the affected and dominant upper limb improved significantly. CONCLUSIONS AND RELEVANCE: PULIT effectively increases children's dexterity of the impaired and dominant upper limb, improves ADL performance, and achieves individual goals. This retrospective analysis could serve as a basis for a future randomized trial. What This Article Adds: This article informs occupational therapy practitioners about a therapy program that includes conventional and rehabilitation technology interventions and enables children with hemiparesis of the upper limb to improve relevant ADL tasks in 8 days' time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".