Pulmonary Fibrosis Followed by Severe Pneumonia in Patients with COVID- 19 infection: A Prospective Multicentre Study
Bibliographic record
Abstract
Abstract Backgrounds : The management of lung complications, especially fibrosis, after coronavirus disease (COVID-19) pneumonia, is an important issue in the COVID-19 post-pandemic era. We aimed to investigate risk factors for pulmonary fibrosis development in patients with severe COVID-19 pneumonia. Methods Clinical and radiologic data were prospectively collected from 64 patients who required mechanical ventilation due to COVID-19 pneumonia and were enrolled from eight hospitals in South Korea. Fibrotic changes on chest computed tomography (CT) was evaluated by visual assessment, and extent of fibrosis (mixed disease score) was measured using automatic quantification system. Results Sixty-four patients were enrolled, and their mean age was 58.2 years (64.1% were males). On chest CT (median interval: 60 days [interquartile range, IQR; 41–78 days] from enrolment), 35 (54.7%) patients showed ≥ 3 fibrotic lesions. The most frequent fibrotic change was traction bronchiectasis (47 patients, 73.4%). Median extent of fibrosis measured by automatic quantification was 10.6% (IQR, 3.8–40.7%). In a multivariable Cox proportional hazard model, which included nine variables with a p-value of < 0.10 in an unadjusted analysis as well as age, sex, and body mass index, male sex (hazard ratio [HR], 3.01; 95% confidence interval [CI], 1.27–7.11) and higher initial sequential organ failure assessment (SOFA) score (HR, 1.18; 95% CI, 1.02–1.37) were independently associated with pulmonary fibrosis (≥ 3 fibrotic lesions). Conclusion Our data suggests that male gender and higher SOFA score at intensive care unit admission were associated with pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".