Motor Function Predicts Parent‐Reported Musculoskeletal Pain in Children with Cerebral Palsy
Bibliographic record
Abstract
BACKGROUND: The relationship between pain and motor function is not well understood, especially for children and adolescents with communication and motor impairments associated with cerebral palsy (CP). OBJECTIVES: To determine whether a predictive relationship between motor function and musculoskeletal pain exists in children with CP. METHODS: Following informed consent, caregivers of 34 pediatric patients with CP (mean [± SD] age 9.37±4.49 years; 80.0% male) completed pain- and function-related measures. Parents completed the Dalhousie Pain Interview and the Brief Pain Inventory based on a one-week recall to determine whether pain had been experienced in the past week, its general description, possible cause, duration, frequency, intensity and interference with daily function. The Gross Motor Function Classification System (GMFCS) was used to classify the motor involvement of the child based on their functional ability and their need for assistive devices for mobility. RESULTS: GMFCS level significantly predicted parent-reported musculoskeletal pain frequency (P<0.02), duration (P=0.05) and intensity (P<0.01). Duration of pain was significantly related to interference with activities of daily living (P<0.05). CONCLUSIONS: Children with CP with greater motor involvement, as indexed by GMFCS level, may be at risk for increased pain (intensity, frequency and duration) that interfers with activities of daily living. The clinical index of suspicion should be raised accordingly when evaluating children with developmental disability who cannot self-report reliably.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".