Long-Term Impact of Hospitalization for Kawasaki Disease on Health-Related Quality of Life
Bibliographic record
Abstract
OBJECTIVE: To prospectively evaluate the long-term impact of Kawasaki disease (KD) hospitalization on health-related quality of life (HRQoL). METHODS: We merged the Outcomes Assessment Program and KD databases and queried for KD admissions between 1 month and 18 years of age. Patients with a diagnosis of community-acquired pneumonia were included as a comparison group. HRQoL was evaluated with the parent proxy Pediatric Quality of Life Inventory (PedsQL). Long-term follow-up PedsQL surveys were performed at least 1 year after initial diagnosis and hospitalization. Results for the entire cohort adjusted for significant differences were calculated. Propensity score-matched cohorts were constructed from the unmatched cohorts of patients with long-term survey responses. Subgroup analysis for the KD group was performed. RESULTS: Patients with KD (n = 61) versus pneumonia (n = 80) had a lower PedsQL total score on admission and experienced a significantly greater HRQoL decline from baseline to admission. At long-term follow-up, no difference occurred in HRQoL between patients with KD and pneumonia, and 89% of patients with KD reached their baseline PedsQL scores. KD diagnostic subtype, coronary artery dilatation, and need for longer follow-up were not associated with HRQoL outcomes at any time point. Intravenous immunoglobulin nonresponders demonstrated lower HRQoL at admission, which did not persist at follow-up. CONCLUSIONS: Children with KD experience acute and significant HRQoL impairment exceeding that of children with newly diagnosed pneumonia, but the scores return to baseline at long-term follow-up. The recoveries at short- and long-term intervals are similar to patients with pneumonia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".