Clinical implications of immune‐mediated diseases in children with Down syndrome
Bibliographic record
Abstract
Children with Down syndrome have changes in their innate and adaptive immunity, which contribute to increased rates of infections, autoimmune diseases, and haematological malignancies. While improved care for congenital heart disease has decreased mortality and morbidity, complications related to immune-mediated diseases continue to limit the life expectancy in Down syndrome. Infectious diseases are common and have a significant effect on development, behaviour and quality of life. Infection frequency and severity are influenced by various anatomical and physiological alterations in addition to immunological changes in Down syndrome. Thus, prevention of respiratory tract infections requires a multifactorial approach. This could include additional active and/or passive immunizations, prophylactic antibiotics, immunoglobulin replacement and ear, nose and throat surgical interventions. Autoimmune conditions like coeliac disease, type I diabetes mellitus and thyroid disease are classically mentioned in the context of Down syndrome. However, autoinflammatory conditions are more prevalent as well. Screening for autoimmune diseases is required and immunosuppression has to be used with caution. Future studies should address optimal screening programmes for immune-mediated diseases in individuals with Down syndrome, as well as the effect of immune modulation, to further decrease morbidity and mortality, and improve the quality of life of individuals with Down syndrome.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".