<scp>MRI</scp> Assessment of Lung Water Density in Individuals Previously Infected With <scp>COVID</scp>‐19: A Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND: Lung damage in post-acute COVID-19 is a common clinical finding. Lung water density (LWD) imaging using ultrashort echo time (UTE) MRI with proton-density weighting is sensitive to edema and fibrosis. PURPOSE: To characterize LWD in COVID-19 survivors, compared with a healthy cohort. STUDY TYPE: Retrospective cohort. POPULATIONS: 185 COVID-19 survivors (63 male; age [median (interquartile range, IQR)]: 51 (25-83) years; 160 (66-363) days from COVID-19 infection to MRI) and 109 healthy controls (64 male; age: 52 (27-76) years) with no history of COVID-19 infection. FIELD STRENGTH/SEQUENCE: 2.89T; Yarnball UTE pulse sequence. ASSESSMENT: Free-breathing three-dimensional LWD images were acquired in both cohorts. Clinical demographics (age, sex, body mass index [BMI]), presence of comorbidities (hypertension, dyslipidemia, diabetes, obesity), COVID-19 hospitalization, pulmonary function, six-minute walking distance, and plasma biomarkers were recorded. STATISTICAL TESTS: Student's t-tests or Mann-Whitney U tests were used to compare lung water metrics between cohorts. The effect of comorbidities was assessed using Kruskal-Wallis tests followed by pairwise Wilcoxon tests with Bonferroni correction. Categorical variables were compared using chi-squared tests. p < 0.05 was considered significant. RESULTS: ) (32 (5) versus 26 (4) in men, 33 (9) versus 26 (7) in women), incidence of comorbidities (78% vs. 50% in men, 72% vs. 38% in women), rates of COVID-19 hospitalization (52% vs. 23% in men, 38% vs. 18% in women), and elevated CRP (mg/L) (2.2 (3.4) vs. 1.1 (1.4) in men, 1.8 (4.2) vs. 1.2 (2.1) in women). DATA CONCLUSION: MRI-derived LWD is elevated in COVID-19 survivors and is related to high BMI, COVID-19 hospitalization, inflammatory plasma biomarkers, and the presence of comorbidities. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".