Increased luminal area of large conducting airways in patients with COVID-19 and post-acute sequelae of COVID-19 A retrospective case-control study
Bibliographic record
Abstract
Summary Background Coronavirus disease 2019 (COVID-19) is associated with enlarged luminal areas of large conducting airways. In 10-30% of patients with acute COVID-19 infection, symptoms persist for more than 4 weeks (referred to as post-acute sequelae of COVID-19, or PASC), and it is unknown if airway changes are associated with this persistence. Thus, we aim to investigate if luminal area of large conducting airways is different between PASC and COVID-19 patients, and healthy controls. Methods In this retrospective case-control study seventy-five patients with PASC (48 females) were age-, height-, and sex-matched to 75 individuals with COVID-19 and 75 healthy controls. Using three-dimensional digital reconstruction from computed tomography imaging, we measured luminal areas of seven conducting airways, including trachea, right and left main bronchi, bronchus intermediate, right and left upper lobe, and left lower lobe bronchi. Findings Airway luminal areas between COVID-19 and PASC groups were not different (p>0.66). There were no group differences in airway luminal area (PASC vs control) for trachea and right main bronchus. However, in the remaining five airways, airway luminal areas were 12% to 39% larger among PASC patients compared to controls (p<0.05). Interpretation Patients diagnosed with COVID-19 and PASC have greater airway luminal area in most large conducting airways compared to healthy controls. No differences in luminal area between patients with COVID-19 and PASC suggest persistence of changes or insufficient time for complete reversal of changes. Funding National Heart, Lung, and Blood Institute (F32HL154320 to JWS; 5R35HL139854 to MJJ); Postdoctoral Fellowship from the Natural Sciences and Engineering Research Council of Canada (AHR).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".