Endocrine Disorders and COVID-19 Severity in Pediatric Populations: A Systematic Review
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted global health systems, with emerging evidence suggesting unique implications for pediatric populations with endocrine disorders. While children generally experience milder acute COVID-19 symptoms, those with pre-existing endocrine conditions may face heightened risks due to the interplay between viral infection and endocrine homeostasis. This systematic review aimed to synthesize evidence on the relationship between endocrine disorders and COVID-19 severity in children, focusing on disease outcomes, metabolic control, and management challenges during the pandemic. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a systematic search was conducted across PubMed, Scopus, Web of Science, and Cochrane Library. Ten studies meeting the inclusion criteria were selected after screening 638 records. Data were extracted on study characteristics, patient demographics, endocrine disorders, COVID-19 severity outcomes, and key findings. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) tool. A narrative synthesis was performed due to heterogeneity in study designs and outcomes. The review revealed significant pandemic-related disruptions in pediatric endocrine health, including increased central precocious puberty cases and elevated BMI z-scores in children with obesity. Diabetes outcomes were mixed: type 1 diabetes patients had better mortality prognoses than type 2 diabetes patients, but diabetic ketoacidosis rates surged. Thyroid dysfunction and stable medication adherence in congenital adrenal hyperplasia were also noted. Risk of bias varied, with three studies rated low, five rated moderate, and two rated as high risk of bias. COVID-19 exacerbated endocrine disorders in children through direct viral effects and indirect lifestyle and healthcare disruptions. The findings underscore the need for adaptive care strategies, including telehealth and mental health support, to mitigate long-term impacts. Future research should prioritize prospective studies to evaluate sustained effects and interventions for at-risk populations.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".