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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".