<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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".