Abstract O.02: Validation of Kawasaki Disease Incidence Assessment as Derived from Health System Administrative Databases vs. Active Retrospective Surveillance in Ontario, Canada
Bibliographic record
Abstract
Introduction: Historically, 2 methods have been used to determine the incidence of Kawasaki disease (KD): active or passive surveillance, or the use of administrative databases. Given the increasing regulatory requirements, mainly around patient privacy, periodic retrospective surveillances have become increasingly challenging. Administrative databases are not curated datasets and doubts have been cast on their accuracy. Methods: The Hospital for Sick Children has been conducting retrospective triennial surveillances of KD since 1995 by contacting all hospitals in Ontario and manually reviewing all cases through chart review, reconciling inter-hospital transfers and multiple readmissions. We queried the Canadian hospital discharge database (Canadian Institute for Health Information) for hospitalizations associated with a diagnosis of KD between 2004-9. The administrative dataset was manually reviewed; patient national health number, institution and dates of admission/discharge were used to identify inter-hospital transfers, readmission and follow-up episodes. Results: The Canadian hospital discharge database reported 1,685 admissions during the study period (281±44 per year) for Ontario. Manual review of the dataset identified 219 (13%) as inter-hospital transfers (56, 26%), readmissions (122, 56%), admissions for follow-up of coronary artery aneurysms (14, 6%) or hospital admissions not related to KD (27, 12%). When these admissions were removed, the total number of incident cases for the study period was 1,466 (244±45 per year). The retrospective triennial surveillance identified 1,373 KD cases during the same period (229±33 per year). The Canadian hospital discharge database overestimated the number of cases in all 6 years by an average of 6.7±5.9%. The overestimation likely comes from patients who were originally diagnosed with KD but in whom the diagnosis of KD was subsequently excluded (historically ~5-6%). Conclusions: Reliance on administrative data to determine incidence of KD is possible and accurate; data should be manually reviewed to remove non-incident cases and estimates should be adjusted to reflect the expected proportion of patients in whom the diagnosis of KD will be subsequently excluded.
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".