Chest computed tomography findings in hospitalized COVID-19 patients: a systematic review and meta-analysis.
Bibliographic record
Abstract
Most studies evaluating chest computed tomography (CT) features in coronavirus disease 2019 (COVID-19) have been small-sized and have presented varied findings. We aim to systematically review these studies and to conduct a meta-analysis of their results to provide a well-powered assessment of chest CT findings in patients with COVID-19. PubMed and EMBASE databases were systematically searched to identify published studies that evaluated chest CT findings in COVID-19 patients. Data regarding study characteristics and CT findings, including distribution of lesions, the lobe of lung involved, lesion densities, and radiological patterns, were extracted. Arcsine transformed proportions from individual studies were pooled using a random-effects model to derive pooled proportions (PPs) and 95% confidence intervals (CIs). A total of fifty-four studies (n=2693 confirmed COVID-19 patients) were included in the final review. Prevalence of different CT findings varied across studies; however, the most common findings were bilateral pulmonary involvement (PP: 74.1% [68.4%, 79.5%]; I2 = 85.76%), ground glass opacification (PP: 64.6% [57.6%, 71.4%]; I2 = 91.52%), involvement of the left lower lobe (PP: 71.2% [58.9%, 82.1%]; I2 = 90.91%), and subpleural distribution of lesions (PP: 57.2% [39.0%, 74.3%]; I2 = 93.08%). Multivariate meta-regression revealed a positive association between prevalence of air bronchograms and average age of the population (p=0.013). Bilateral ground glass opacification, a subpleural distribution of lesions, and involvement of the left lower lobe were the most notable chest CT findings in COVID-19 patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.005 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.000 | 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 teacher head, 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".