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.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".