Pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2.
Bibliographic record
Abstract
Second wave of the new coronavirus (SARS-CoV-2) has been declared throughout the world. It has been always thought that children are the least affected group. A new childhood disease, referred to as MIS-C (Multisystem Inflammation Syndrome) or PIMS-TS (Pediatric Inflammatory Multiorgan Syndrome Temporally related to SARS-CoV-2) was first recognized in April 2020. Shock and multiorgan failure affected some of those children that required intensive care; others were clinically similar to Kawasaki disease or toxic shock. The clinical evidence suggests that this inflammatory multisystem syndrome is temporally associated with severe acute respiratory syndrome corona virus 2. Many clinical uncertainties regarding this new disease rapidly became apparent in prevalence, clinical phenotypes, variable severity, clinical course, and optimal management. We aim to increase awareness of this syndrome regarding the diagnosis and management of children with suspected PIMS-TS by presenting two clinical cases and illustrating the available medical literature in regards to establishing the diagnosis and the appropriate therapeutic interventions. SARS-Cov-2 related medical impacts on children seem not well clarified yet. When a PIMS-TS case is suspected then full investigations should be done, children who have persistent fever associated with abdominal pain, diarrhea ,vomiting ,cough, neurologic symptoms should have primary blood tests to identify PIMS-TS: full blood count, CRP: C-reactive protein, BUN: Blood Urea Nitrogen, Cr: Creatinine, Electrolytes and liver function. Multidisciplinary team approach seems mandatory from the very beginning. Despite the use of IVIG in the treatment of all diagnosed cases, steroids in regular doses could be a good alternative and requires further investigative evaluations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".