COVID-19 in children across three Asian cosmopolitan regions
Bibliographic record
Abstract
As another wave of COVID-19 outbreak has approached in July 2020, a larger scale COVID-19 pediatric Asian cohort summarizing the clinical observations is warranted. Children confirmed with COVID-19 infection from the Republic of Korea, the Hong Kong Special Administrative Region (HKSAR) and Wuhan, China, during their first waves of local outbreaks were included. Their clinical characteristics and the temporal sequences of the first waves of local paediatric outbreaks were compared. Four hundred and twenty three children with COVID-19 were analyzed. Wuhan had the earliest peak, followed by Korea and HKSAR. Compared with Korea and Wuhan, patients in HKSAR were significantly older (mean age: 12.9 vs. 10.8 vs. 6.6 years, p < 0.001, respectively) and had more imported cases (87.5% vs. 16.5% vs. 0%, p < 0.001, respectively). The imported cases were also older (13.4 vs. 7.6 years, p < 0.001). More cases in HKSAR were asymptomatic compared to Korea and Wuhan (45.5% vs. 22.0% vs. 20.9%, p < 0.001, respectively), and significantly more patients from Wuhan developed fever (40.6% vs. 29.7% vs. 21.6%, p=0.003, respectively). There were significantly less imported cases than domestic cases developing fever after adjusting for age and region of origin (p = 0.046). 5.4% to 10.8% of patients reported anosmia and ageusia. None developed pediatric multisystem inflammatory syndrome temporally associated with SARS-CoV-2 (PMIS-TS). In general, adolescents were more likely to be asymptomatic and less likely to develop fever, but required longer hospital stays. In conclusion, majority patients in this pediatric Asian cohort had a mild disease. None developed PIMS-TS. Their clinical characteristics were influenced by travel history and age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.009 | 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".