Bibliographic record
Abstract
PURPOSE OF REVIEW: Coronary artery damage resulting from Kawasaki's disease is the leading cause of acquired heart disease in children in the developed world. This review highlights advances in our understanding of the etiology of Kawasaki's disease, the immune response leading to vascular damage, potential biomarkers, and insights into mechanisms of disease addressed by an animal model. Clinical dilemmas are discussed in the context of the new American Heart Association recommendations for the diagnosis and treatment of Kawasaki's disease. RECENT FINDINGS: Improved understanding of the mechanisms of disease will assist in identifying predisposed individuals and in development of more effective therapy. Most investigators agree that an infectious trigger leads to massive activation of the immune system, resulting in a prolonged self-directed immune response at the coronary arteries. The etiology debate has centered on the nature of and mechanisms involved in immune activation. Genetic studies have not provided conclusive answers to these questions. Mechanistic studies done in animal models have pointed to specific biologic factors critical for coronary artery damage and together with studies in children may lead to more rationally conceived biologically based interventions. Increasingly, the questions regarding clinical management address timing of therapy and the management of children presenting with atypical Kawasaki's disease. New guidelines and management algorithms have been proposed by the American Heart Association to address these concerns. SUMMARY: Biochemical and phenotypic characterization of Kawasaki's disease continues to improve. Answers are closer on etiology, reliable biomarkers, valid predictors of coronary outcome, and improved treatment of this syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".