Pain and hospital admissions are important factors associated with quality of life in nonambulatory children
Bibliographic record
Abstract
AIM: This was the first study to investigate the factors associated with health-related quality of life (HRQoL) in nonambulatory children with cerebral palsy (CP), based on a HRQoL measure specifically developed for this population. METHODS: The Dutch version of the Caregiver Priorities and Child Health Index of Life with Disabilities (CPCHILD-DV) was used to measure HRQoL. It was completed by 66 parents of 47 boys and 19 girls with nonambulatory CP aged between five and 18 years with gross motor function classification system (GMFCS) levels of IV and V. Factors measured were the child's motor and cognitive impairments, comorbidities, pain, parents' education and occupations and family structure. Multiple linear regression analyses were used to determine the significant factors and the relative contribution of these factors to the CPCHILD-DV scores. RESULTS: The most important factors associated with poorer HRQoL scores were pain and hospital admissions in the previous six months. Other factors were as follows: increased GMFCS level, feeding by gastrostomy tube, inability to communicate verbally, cognitive impairment, poor seizure control and higher parents' educational qualifications. CONCLUSION: Pain and hospital admissions were the most important factors that were negatively associated with HRQoL in nonambulatory children with CP between five to 18 years.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".