Fostering Global Collaboration Around Kawasaki Disease. Reflections From the 14th International Kawasaki Disease Symposium
Bibliographic record
Abstract
the 14th International Kawasaki Disease Symposium (IKDS) took place in Montreal, Canada.This symposium, first held in 1984 in Hawaii, represents a long-standing history of an international meeting where clinicians and scientific investigators passionate about Kawasaki disease (KD) gather to share and solve the mysteries of a half-a-century-old acute inflammatory disease of childhood that can have life-threatening coronary artery complications. 1 The disease, initially described by a constellation of its clinical features (the mucocutaneous lymph node syndrome), carries the name of Doctor Tomisaku Kawasaki, the astute clinician who first described a cohort of 50 children in 1967.Since then, KD has grown to be the most common cause of acquired heart disease in most regions of the world.2e5 However, it is still a disease that is underserved in many parts of the world, with limited resources available to study its cause, bring diagnostic tools to market in partnership with industry sponsors, and develop new therapies.Given this, children with KD are still at high risk of being misdiagnosed or not having access to treatments to prevent life-threatening coronary artery aneurysms.In spite of all of this, clinicians, scientists, and patient advocates have been working for over 50 years to advance the care of children with KD worldwide.The scientific endeavours have ranged from animal to clinical research and from genetics to anthropology.The advances in KD research have helped improve pediatric health beyond just KD, as recently evidenced by the use of novel antiinflammatory therapies whose safety had been proven in KD for multisystem inflammatory syndrome in children triggered by the SARS-CoV-2 virus.6e8
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 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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.032 | 0.053 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".