The landscape of inter-institutional and multinational collaborations in Kawasaki disease
Bibliographic record
Abstract
Objectives: Integrated research potentiates evidence-based knowledge, particularly for uncommon diseases such as Kawasaki disease (KD). The 14th International Kawasaki Disease Symposium focussed on "Fostering global collaborations to solve KD," circulated a 23-question survey highlighting existing collaborations. Methods: Information was collected from KD collaborative groups. One or more of the following characteristics defined a collaborative effort: recurrent clinical trials collaborations; international, national, or inter-state research; multi-institutional research; national, government-funded or government-appointed research; epidemiology group; focus group on KD practice and science or other health organization chapters. Results: Responses from 21 groups were limited to a single country (67%). The groups had formed between 1991 and 2022. The multi-institutional groups included a median of 1 to 20 countries composed of 2-150 institutions. The groups included 10-20 active members (38%), 20-50 members (38%), 50-100 (14%), or more than 100 members (10%). A majority (67%) of collaborations produced 2-11 peer-reviewed publications. Groups operated under a centralized governance structure (62%), with a steering committee (92%), bylaws (23%), membership fees (8%), or another structure (8%). Only 33% had a written mission statement. While 48% had no formal funding sources, the remainder were funded by granting agencies (29%), governmental funding (24%), private donations and fundraising drives (19%), industry support (10%), and other sources (5%). Conclusions: The majority of polled collaborative KD groups are multicenter and national. Despite the lack of funding, most groups demonstrate successful collaborations that result in peer-reviewed publications. There is a need for advocacy to support funding for these important collaborative groups.
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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 | 0.053 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".