Health-related quality of life in children with inflammatory brain disease
Bibliographic record
Abstract
OBJECTIVE: To quantify the impact of inflammatory brain diseases in the pediatric population on health-related quality of life, including the subdomains of physical, emotional, school and social functioning. METHODS: This was a multicenter, observational cohort study of children (< 18 years of age) diagnosed with inflammatory brain disease (IBrainD). Patients were included if they had completed at least one Health Related Quality of Life Questionnaire (HRQoL). HRQoL was measured using the Pediatric Quality of Life Inventory Version 4.0 (PedsQL) Generic Core Scales, which provided a total score out of 100. Analyses of trends were performed using linear regression models adjusted for repeated measures over time. RESULTS: In this study, 145 patients were included of which 80 (55%) were females. Cognitive dysfunction was the most common presenting symptoms (63%), and small vessel childhood primary angiitis of the CNS was the most common diagnosis (33%). The mean child's self-reported PedsQL total score at diagnosis was 68.4, and the mean parent's proxy-reported PedsQL score was 63.4 at diagnosis. Child's self-reported PedsQL scores reflected poor HRQoL in 52.9% of patients at diagnosis. Seizures or cognitive dysfunction at presentation was associated with statistically significant deficits in HRQoL. CONCLUSION: Pediatric IBrainD is associated with significantly diminished health-related quality of life. Future research should elucidate why these deficits occur and interventions should focus on improving HRQoL in the most affected subdomains, in particular for children presenting with seizures and cognitive dysfunction.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".