Severe coronavirus disease 2019 in pediatric solid organ transplant recipients: Big data convergence study in Korea (K-COV-N cohort)
Bibliographic record
Abstract
OBJECTIVES: The risk of severe COVID-19 in children with a solid organ transplant (SOT) is not well established. We compare the relative risk of severe COVID-19 infection between pediatric SOT and non-SOT children. METHODS: The newly constructed K-COV-N cohort (Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service) was used. Children with COVID-19 (<18 years old) who underwent SOT between January 2008 to January 2022 were included. Non-SOT children with COVID-19 were selected in a ratio of 1:4 using propensity score matching. Three definitions of severe COVID-19 were established based on their requirement for respiratory support: severe I (requiring respiratory support above a high-flow nasal cannula or prolonged hospitalization ≥6 days), severe II (requiring any oxygen supplement), and severe III (requiring any oxygen supplement or prolonged hospitalization ≥6 days). RESULTS: Among 2,957,323 children with COVID-19, 206 pediatric SOT recipients (SOTRs) were identified and included in the analysis along with 803 matched non-SOT children. Most infections (96.6%) occurred during the Omicron period; no cases of mortality were reported. Pediatric SOTR had a 3.6-fold (95% confidence interval = 1.1-11.7, P = 0.03) higher risk of severe I, and a 4.9-fold (95% confidence interval = 1.6-15.0, P = 0.006) higher risk of severe III than non-SOT children. No cases of severe II occurred in the non-SOT children. Although not statistically significant, no severe COVID-19 cases were reported in the vaccinated SOT group (0.0% vs 5.7%, P = 0.09 in severe III). CONCLUSION: Pediatric SOTRs have a significantly higher risk of severe COVID-19 than non-SOT children. Our findings support the need for tailored strategies for these high-risk 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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".