High intelligence may exacerbate paediatric inflammatory response to SARS-CoV-2 infection
Bibliographic record
Abstract
The body's innate and acquiredimmunesystems are critical in responses to a wide spectrum of assaults, including SARS-CoV-2 infection. We identify studies of autoimmunity to support our hypothesis that a high intelligence quotient (IQ) may put children at increased risk for severe COVID-19 sequelae; especially those whose viral load is high and/or who develop multisystem inflammatory syndrome in children (MIS-C). MIS-C is associated with a higher risk of COVID-19 morbidity and death, even in otherwise healthy children. As information and evidence about SARS-CoV-2 infection continue to expand, our hypothesis suggests adding a potentially intriguing piece to the pandemic puzzle for further investigation. Drawing on a select review of published research and case reports, we discuss immune dysregulation in paediatric patients with a high IQ, including post-infection cytokine expression in the myocardium. Further, we provide a review of 27 paediatric (≤19 years; median age 16) cases of severe COVID-19 outcomes, drawn from media sources published between March and September 2020, in which we identify possible evidence of a 'hyper brain, hyper body' response to infection. We aver these cases are noteworthy given that paediatric death with COVID-19 disease is remarkably rare, and the estimated prevalence of a high IQ (or giftedness) is only 2% in the general population. These observations warrant prospective and retrospective studies of autoinflammatory markers and mechanisms to elucidate any special psychoneuroimmunological vulnerability in children with a high IQ, as such studies may raise implications for how and when prophylactic medical care is provided to children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".