Predictors of health-related quality of life following injury in childhood and adolescence: a pooled analysis
Bibliographic record
Abstract
BACKGROUND: Injury is a leading contributor to the global disease burden in children and places children at risk for adverse and lasting impacts on their health-related quality of life (HRQoL) and development. This study aimed to identify key predictors of HRQoL following injury in childhood and adolescence. METHODS: Data from 2259 injury survivors (<18 years when injured) were pooled from four longitudinal cohort studies (Australia, Canada, UK, USA) from the paediatric Validating Injury Burden Estimates Study (VIBES-Junior). Outcomes were the Paediatric Quality of Life Inventory (PedsQL) total, physical, psychosocial functioning scores at 1, 3-4, 6, 12, 24 months postinjury. RESULTS: Mean PedsQL total score increased with higher socioeconomic status and decreased with increasing age. It was lower for transport-related incidents, ≥1 comorbidities, intentional injuries, spinal cord injury, vertebral column fracture, moderate/severe traumatic brain injury and fracture of patella/tibia/fibula/ankle. Mean PedsQL physical score was lower for females, fracture of femur, fracture of pelvis and burns. Mean PedsQL psychosocial score was lower for asphyxiation/non-fatal submersion and muscle/tendon/dislocation injuries. CONCLUSIONS: Postinjury HRQoL was associated with survivors' socioeconomic status, intent, mechanism of injury and comorbidity status. Patterns of physical and psychosocial functioning postinjury differed according to sex and nature of injury sustained. The findings improve understanding of the long-term individual and societal impacts of injury in the early part of life and guide the prioritisation of prevention efforts, inform health and social service planning to help reduce injury burden, and help guide future Global Burden of Disease estimates.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".