Kawasaki Disease Shock Syndrome: A Challenging Diagnosis
Bibliographic record
Abstract
The authors present a clinical report of a previously healthy 4-year-old girl, admitted to a pediatric intensive care unit (PICU) presenting with a fluid-refractory shock. She was initially admitted in the pediatric emergency department with a history of fever and petechial rash and an initial diagnosis of invasive meningococcal disease was placed. During the PICU stay, acute myocarditis with episodes of supraventricular tachycardia and lateral and inferior wall myocardial ischemia, ileitis and serositis were noted. At 14th day of stay, she developed peeling on the hands and feet and, aneurysmatic ectasia of the coronary arteries were visualized in transthoracic echocardiography, allowing the diagnosis of Kawasaki disease shock syndrome (KDSS). The child improved after empirical treatment with antibiotics, intravenous immunoglobulin and corticosteroid therapy. She was started on antiplatelet therapy due to changes in the coronary arteries. Early recognition of KDSS can be challenging; however, a delayed diagnosis increases the risk of coronary changes and a fatal outcome. Int J Clin Pediatr. 2021;10(1):10-17 doi: https://doi.org/10.14740/ijcp425
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".