Rapid review of multisystem inflammatory syndrome in paediatrics: What we know one year later
Bibliographic record
Abstract
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) associated with coronavirus disease 2019 (COVID-19) is an emerging condition that was first identified in paediatrics at the onset of the COVID-19 pandemic. The condition is also known as pediatric inflammatory multisystem syndrome temporally associated with severe acute respiratory syndrome coronavirus 2 (PIMS-TS or PIMS), and multiple definitions have been established for this condition that share overlapping features with Kawasaki Disease and toxic shock syndrome. METHODS: A review was conducted to identify literature describing the epidemiology of MIS-C, published up until March 9, 2021. A database established at the Public Health Agency of Canada with COVID-19 literature was searched for articles referencing MIS-C, PIMS or Kawasaki Disease in relation to COVID-19. RESULTS: A total of 195 out of 988 articles were included in the review. The median age of MIS-C patients was between seven and 10 years of age, although children of all ages (and adults) can be affected. Multisystem inflammatory syndrome in children disproportionately affected males (58% patients), and Black and Hispanic children seem to be at an elevated risk for developing MIS-C. Roughly 62% of MIS-C patients required admission to an intensive care unit, with one in five patients requiring mechanical ventilation. Between 0% and 2% of MIS-C patients died, depending on the population and available interventions. CONCLUSION: Multisystem inflammatory syndrome in children can affect children of all ages. A significant proportion of patients required intensive care unit and mechanical ventilation and 0%-2% of cases resulted in fatalities. More evidence is needed on the role of race, ethnicity and comorbidities in the development of MIS-C.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".