Relationship Between Physical Activity, Tic Severity and Quality of Life in Children with Tourette Syndrome.
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between physical activity, tic severity and quality of life (QoL) in children and adolescents with persistent tic disorder and Tourette Syndrome. METHOD: Baseline data was examined from a larger randomized controlled trial (Clinicaltrials.gov NCT02153463). Physical activity was assessed via pedometers with daily step count recorded. Tic severity (assessed via Yale Global Tic Severity Scale or YGTSS) and QoL (assessed via PEDs QL 4.0) were compared between those more physically active (≥12,000 steps/day) and less physically active (<12,000 steps/day). RESULTS: Thirteen children participated; four had ≥12,000 steps/day and nine had <12,000 steps/day. The active group had a lower total tic severity (p = 0.02), and total YGTSS score (p=0.01). The vocal tic severity score was lower in the active group (p=0.02). Motor tic severity was not different amongst the two groups. For Peds QL scores, the active group performed better in physical functioning (p=0.01), social functioning (p=0.03), school functioning (p=0.02), psychosocial functioning (p=0.03) and total PEDs QL score (p=0.01). CONCLUSIONS: Higher physical activity levels are associated with lower vocal tic severity and improved aspects of quality of life. Further research is needed to determine the utility of physical activity as therapy for tics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".