Delineating phenotypes of Kawasaki disease and SARS-CoV-2-related inflammatory multisystem syndrome: a French study and literature review
Bibliographic record
Abstract
OBJECTIVE: To better define the clinical distinctions between the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related paediatric inflammatory multisystem syndrome (PIMS) and Kawasaki disease (KD). METHODS: We compared three groups of patients: group 1, cases from our national historic KD database (KD-HIS), before the SARS-CoV-2 pandemic; group 2, patients with KD admitted to an intensive care unit (KD-ICU) from both our original cohort and the literature, before the SARS-CoV-2 pandemic; and group 3, patients with PIMS from the literature. RESULTS: KD-HIS included 425 patients [male:female ratio 1.3, mean age 2.8 years (s.d. 2.4)], KD-ICU 176 patients [male:female ratio 1.3, mean age 3.5 years (s.d. 3.1)] and PIMS 404 patients [male:female ratio 1.4, mean age 8.8 years (s.d. 3.7)]. As compared with KD-HIS patients, KD-ICU and PIMS patients had a higher proportion of cardiac failure, digestive and neurological signs. KD-ICU and PIMS patients also had a lower frequency of typical KD-mucocutaneous signs, lower platelet count, higher CRP and lower sodium level. As compared with KD-HIS and KD-ICU patients, PIMS patients were older and more frequently had myocarditis; they also had fewer coronary abnormalities and lower sodium levels. Unresponsiveness to IVIG was more frequent in KD-ICU than KD-HIS and PIMS patients. CONCLUSION: On clinical grounds, KD-HIS, KD-ICU and PIMS might belong to a common spectrum of non-specific pathogen-triggered hyperinflammatory states. The causes of increasing inflammation severity within the three entities and the different effects on the heart remain to be determined.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".