Absence of Severe Complications From SARS-CoV-2 Infection in Children With Rheumatic Diseases Treated With Biologic Drugs
Bibliographic record
Abstract
To the Editor: We read with interest the editorial by Cron and Chatham1 suggesting a cytokine storm syndrome (CSS) occurring in response to SARS-CoV-2 infection and, consequently, a possible role for targeted approaches to blocking inflammatory cytokines. Almost 30% of patients with coronavirus disease 2019 (COVID-19) develop severe acute respiratory distress syndrome with a high mortality rate2. In those critically ill patients, there are clinical signs and symptoms, as well as laboratory abnormalities, that suggest a CSS is occurring in response to the viral infection1. In contrast to adults, pediatric patients with COVID-19 seem to have a milder clinical course and asymptomatic SARS-CoV-2 infections may be frequent3. However, albeit rarely, severe infections may occur even in children, with pediatric intensive care unit admission or high-flow ventilation. In a recent Spanish cohort, 60% of confirmed infections in children required hospitalization4. COVID-19 pediatric transmission routes include close contact with family members, exposure to epidemic areas, or … Address correspondence to Dr. G. Filocamo, Pediatric Rheumatology, Pediatric Medium Intensity Care Unit, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Clinica De Marchi, Via della Commenda, 9, 20122 Milan, Italy. Email: giovanni.filocamo{at}policlinico.mi.it.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.011 | 0.009 |
| 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".