Bibliographic record
Abstract
Aim of the review: To review major epidemiological aspects of Kawasaki disease (KD) in Europe, describing demographic characteristics, revising its incidence along with time trends and geographic variations, and describing migration studies to provide clues about its etiology. Recent findings: The annual incidence of KD in Europe is about 10–15 per 100,000 children under 5 years old and seems to be relatively stable over time and space. Demographic characteristics are in line with those in other countries of the world, with a higher incidence in children from Asia and possibly North African origin. All studies performed across Europe found a coherent seasonal distribution of KD onset peaking from winter to early spring. This seasonal distribution was consistent over the years and suggests a climate-related environmental trigger. The occurrence of peaks during pandemics, microbiological findings and a possible link with southerly winds support the hypothesis of an airborne infectious agent. Neither other airborne agents such as pollutants or pollens nor urbanization and industrialization seem to have major effect on the etiology. Conclusion: Discrepancies in KD incidence rates across studies were due more to methodological differences, variation in definitions and awareness of the disease than a real increase in incidence. Genetic predisposition is undeniable in KD, but environmental factors seem to play a pivotal role. Several lines of evidence support a non-exclusive airborne infectious agent with a protective immune response by the host as a key factor in inducing the inflammatory cascade responsible for symptoms and complications.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".