Inflammation associated with lung function abnormalities in COVID-19 survivors
Bibliographic record
Abstract
BACKGROUND: Activation of inflammatory pathways promotes organ dysfunction in COVID-19. Currently, there are reports describing lung function abnormalities in COVID-19 survivors; however, the biological mechanisms remain unknown. The aim of this study was to analyze the association between serum biomarkers collected during and following hospitalization and pulmonary function in COVID-19 survivors. METHODS: Patients recovering from severe COVID-19 were prospectively evaluated. Serum biomarkers were analyzed from admission to hospital, peak during hospitalization, and at the time of discharge. Pulmonary function was measured approximately 6 weeks after discharge. RESULTS: 100 patients (63% male) were included (age 48 years, SD ± 14) with 85% having at least one comorbidity. Patients with a restrictive spirometry pattern (n = 46) had greater inflammatory biomarkers compared to those with normal spirometry (n = 54) including peak Neutrophil-to-Lymphocyte ratio (NLR) value [9.3 (10.1) vs. 6.5 (6.6), median (IQR), p = 0.027] and NLR at hospital discharge [4.6 (2.9) vs. 3.2 (2.9) p = 0.005] and baseline C-reactive protein value [164.0 (147.0) vs. 106.5 (139.0) mg/dL, p = 0.083). Patients with an abnormal diffusing capacity (n = 35) had increased peak NLR [8.9 (5.9) vs. 5.6 (5.7) mg/L, p = 0.029]; baseline NLR [10.0 (19.0) vs. 4.0 (3.0) pg/ml, p = 0.002] and peak Troponin-T [10.0 (20.0) vs. 5.0 (5.0) pg/ml, p = 0.011] compared to patients with normal diffusing capacity (n = 42). Multivariable linear regression analysis identified predictors of restrictive spirometry and low diffusing capacity, but only accounted for a low degree of variance in pulmonary function outcome. CONCLUSION: Overexpression of inflammatory biomarkers is associated with subsequent lung function abnormalities in patients recovered from severe COVID-19.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".