Investigating physiological symptoms associated with mental health symptoms in youth with cerebral palsy: An observational study
Bibliographic record
Abstract
Over 50 % of children and youth with cerebral palsy (CP) experience mental health challenges, with anxiety and depression most common. Youth with CP also experience several physiological symptoms such as fatigue, pain, sedentary lifestyle, and sleep disturbances that impact their daily living; however, little is known about the impact of these symptoms on mental health outcomes in these youth. This study addressed this gap and examined the individual and cumulative impacts of physiological symptoms on anxiety and depression symptoms in youth with CP. Forty youth with CP aged 8 to 18 years, and their caregiver, participated in this cross-sectional observational study. Pain, fatigue, anxiety, and depressive symptoms were measured using caregiver- and self-reported questionnaires and participants wore accelerometers for seven consecutive days, providing non-invasive physical activity and sleep pattern data. Youth with CP experienced substantial physiological symptoms and elevated anxiety and depression symptoms. Linear regression models determined that all physiological factors were predictive of caregiver-reported youth anxiety (R2 = 0.23) and youth depressive symptoms (R2 = 0.48). Fatigue, pain severity, sleep efficiency, and physical activity outcomes individually and cumulatively contributed to caregiver-reported youth anxiety and depression symptoms. These findings highlight the important role of physiological symptoms as potential risk factors and potential targets for intervention for mental health issues for in youth with CP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".