Long-term alveolar-capillary diffusion impairments after severe SARS-CoV-2 pneumonia
Bibliographic record
Abstract
BACKGROUND: Persistent respiratory symptoms and impaired gas exchange are common in patients recovering from COVID-19 pneumonia. The Lung Diffusing Capacity for Carbon Monoxide (DLCO) and Carbon Monoxide Transfer Coefficient (KCO) do not adequately distinguish alveolar membrane dysfunction from vascular abnormalities. This study aimed to characterize persistent diffusion impairment in post-ICU patients with prior SARS-CoV-2 pneumonia and reduced DLCO. METHODS: After hospital discharge, patients underwent spirometry, DLCO measurement, and a 6-minute walking test every six months. If DLCO remained impaired at 18-24 months, a combined Lung Diffusing Capacity for Nitric Oxide (DLNO) and DLCO assessment was performed to differentiate alveolar-capillary membrane (DmCO) and pulmonary capillary blood volume (Vc) alterations. RESULTS: Among 20 patients with persistent DLCO reduction, 3 had an obstructive ventilatory pattern, 6 had restriction, and 12 had low KCO. In restrictive cases, KCO was reduced but remained within normal limits without compensation. The DLNO/DLCO ratio exceeded 113.5% predicted in all patients. DmCO was impaired in 7 patients, while Vc was reduced in 16. CONCLUSION: Both DLCO determinants were affected, with vascular impairment predominating. Vc reduction was present in most patients, with mean values below the lower limit of normality, whereas DmCO was less affected and often normal. The elevated DLNO/DLCO ratio suggests that persistent DLCO reduction is primarily driven by prolonged pulmonary capillary circulation dysfunction rather than alveolar membrane alterations, highlighting the vascular component as the primary site of long-term impairment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".