Chest imaging for COVID-19: a single-center comparative results in pediatric patients
Bibliographic record
Abstract
BACKGROUND: Chest computed tomography (CT) appears to be an important radiological modality for the diagnosis of COVID-19 in adults. Studies comparing the findings of such children with those of other viral infections have not been reported either. The aim of this study was to present comparative imaging findings of 75 pediatric COVID-19 patients and four patients with other viral upper respiratory tract infections. We also aimed to demonstrate the possible association between the radiological and laboratory findings in the COVID group. METHODS: From 11 March 2020 to 20 June 2020, 79 children (aged < 18 years) were enrolled. COVID-19 was detected by RT-PCR or antibody testing. A plain chest X-ray was obtained from all subjects. Non-contrast chest CT was performed for symptomatic patients. RESULTS: Seventy-five patients had COVID-19 and 4 were infected with other pathogens i.e. adenovirus, rhinovirus, parainfluenza virus B, respiratory syncytial virus. The ages of the patients (36 M, 43 F) ranged from 7 months to 17 years old. The sensitivity of chest X-ray (as compared to RT-PCR) was 10.67% (95 CI%: 4.72 - 19.94%). From 23 chest CT`s five of them were normal and nine of them had only nodules ( < 5mm). The sensitivity of CT was 78.26% (95CI%: 54.30 - 92.54%), false-negative rate was 21.7%. CONCLUSIONS: The sensitivity of chest CT was found to be low and any significant correlations could have not been depicted, between the radiological parameters and the presence of lymphopenia. Clinical follow-up combined with corresponding pathogen detection, and chest CT of the symptomatic COVID-19 patients might be a feasible/prompt protocol in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".