Abstract 222: North American Kawasaki Disease Registry - Advancing Clinical Research Through Collaboration
Bibliographic record
Abstract
Background: Clinical research in children with Kawasaki disease (KD), particularly those with coronary artery aneurysms (CAA), is challenging due to the limited number of patients available at any single institution. This has resulted in imperfect evidence for optimal management and considerable practice variation. Methods: The North American Kawasaki Disease Registry (NAKDR) was started in July 2013 to determine the prevalence, patient-level and pharmacological risk factors for outcomes of CAA after KD. The NAKDR enrolls KD patients diagnosed from 1999-2013 with CAA (defined as any segment with a z-score >2.5). The NAKDR and its internet-based data entry portal are maintained at The Hospital for Sick Children in Toronto. Local ethics approvals and bilateral data sharing agreements are necessary for participation. Participation in the NAKDR is currently unfunded and voluntary. In September 2014, a survey on the future of the NAKDR was sent to all participating centers (response rate: 54%). Results: 45 sites have been invited, of which 37 (82%) agreed to participate. As of September 2014, 20 sites are actively submitting data; 17 are still being initiated; 706 cases have been submitted. The majority (90%) of members indicated that they wished to continue enrolling newly diagnosed patients and continue follow-up on patients already enrolled. Members were split (45% for, 55% against) as to whether the NAKDR should be expanded to include all KD patients, regardless of CAA status. Finally, while most NAKDR members (60%) indicated that site reimbursement was not an absolute condition for future participation, most members suggested that potential funding sources should be sought to expand/facilitate activities. At term, the NAKDR is expected to comprise ~1,400 patients and, if moving forward, add ~120 new KD patients with CAA per year. Conclusions: The NAKDR is an important tool for clinical research in children with CAAs after KD, and its success represents broad support from clinicians. The next step will be to formalize the NAKDR leadership structure, create standard operating procedures, and pursue prospective studies and funding.
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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".