Delayed Diagnosis of Kawasaki Disease: What Are the Risk Factors?
Bibliographic record
Abstract
OBJECTIVE: Because late diagnosis of Kawasaki disease increases the risk for coronary artery abnormalities, we explored the prevalence of and possible risk factors for delayed diagnosis by using the database of the Pediatric Heart Network trial of corticosteroid treatment for Kawasaki disease. METHODS: We collected sociodemographic and clinical data at presentation for all patients who were treated for presumed Kawasaki disease at 8 centers (7 in the United States, 1 in Canada). Delayed diagnosis was evaluated by total number of illness days to diagnosis and by the percentage of patients who were treated after day 10 of illness. Independent predictors of delayed diagnosis were identified by using multivariate linear and logistic regression. RESULTS: Of the 589 patients who received intravenous immunoglobulin, 27 were treated before screening for the trial and excluded; 562 patients formed the cohort for analysis. Kawasaki disease was diagnosed at 7.9 +/- 3.9 days, 92 (16%) cases after day 10. Centers were similar with respect to patient age and gender. Centers differed in the patient percentage with incomplete Kawasaki disease; clinical criteria of cervical adenopathy, oral changes, and conjunctivitis; and distance of residence from the center. Independent predictors of greater number of illness days at diagnosis included center, age of <6 months, incomplete Kawasaki disease, and greater distance from the center. Independent predictors of diagnosis after day 10 were age of <6 months, incomplete Kawasaki disease, and greater distance). Socioeconomic variables had no association with delayed diagnosis. CONCLUSIONS: Even after adjustment for patient factors, illness duration at diagnosis varies by center. These findings underscore the need to maintain a high index of suspicion of Kawasaki disease in the infant who is younger than 6 months and has prolonged fever even with incomplete criteria. Outreach educational programs may be useful in promoting earlier recognition and treatment of Kawasaki disease.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".