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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".