Evaluation of Chest CT Scan Findings in Pediatric Patients With COVID‐19: A Retrospective Descriptive Study
Bibliographic record
Abstract
ABSTRACT Background and Aims COVID‐19 in children presents with varying severity. Identifying characteristic chest CT features is essential for accurate diagnosis and screening. This study aimed to evaluate CT patterns in pediatric COVID‐19 cases to enhance diagnostic accuracy. Methods This retrospective cross‐sectional study analyzed chest CT scans of 42 children with confirmed COVID‐19 at Children's Hospital, Gorgan, Iran. A 14‐item checklist assessed demographics, lung involvement, and radiological features. Descriptive statistics and Fisher's exact test were used for analysis. Results Ground‐glass opacities (GGO) were the most common finding (92.9%), followed by consolidation (54.8%). Both were significantly associated with peripheral distribution ( p < 0.001) and lower zone involvement ( p < 0.001 for GGO, p ≈ 0.002 for consolidation). Lesions affected peripheral lung zones (45.24%) or both central and peripheral zones (40.48%), with consolidation predominantly in the latter ( p < 0.001). Notably, 7.1% of children had no visible lung lesions. Cases with < 25% lung involvement showed significant correlation with GGO ( p < 0.001). Pleural effusion was observed in 4.8%, while pericardial effusion and mediastinal lymphadenopathy were absent. Conclusion Pediatric COVID‐19 commonly presents with GGO, consolidation, and peripheral lung lesions, with rare occurrence of features such as pleural effusion compared to adults. These differences may refine diagnostic strategies for pediatric 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".