Risk factors associated with length of hospital stay in children and adolescents with coronavirus disease 2019 in Egypt
Bibliographic record
Abstract
Background With the growing incidence of children with coronavirus disease 2019 (COVID-19), the hospitalization rate in this age category has been increasing. This study aimed to assess the length of hospital stay (LOS) among children with COVID-19 and examine potential risk factors. Patients and methods We retrospectively collected data on 50 consecutive children and adolescents with mild to moderate COVID-19 who were discharged after treatment from one hospital in Egypt during July 2020. Betas (Bs) and their 95% confidence intervals (CIs) for the association of patients' sociodemographic and clinical characteristics with their LOS were computed using unadjusted and multivariable-adjusted linear regression models. Results The average LOS was 8.3 days (median 9 days). Presenting with fever, cough, and ground-glass opacity in radiograph was associated with longer LOS in the unadjusted model with Bs (95% CIs): 4.30 (1.07, 7.52), 3.50 (0.34, 6.66), and 5.55 (2.72, 8.37), respectively. In the multivariable-adjusted model, only ground-glass opacity in radiograph remained statistically associated with longer LOS (B = 4.75, 95% CI: 0.31, 9.20). Conclusion Children and adolescents with COVID-19 stayed in the hospital for a relatively short period. Selected clinical and radiological findings may be associated with longer LOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".