Atelectesis on chest CT is associated with worse outcomes in patients with COPD using long-term NIV
Bibliographic record
Abstract
Background: Long-term non-invasive ventilation (LT-NIV) may reduce the number of exacerbations and prolong survival in patients with COPD. Atelectasis is frequently reported on chest CT scan in these patients; however, the prognostic impact of this finding is not fully understood. Aims and Objectives: The aim of this project is to investigate the prevalence of atelectasis in patients with COPD on LT-NIV and its clinical impact. Methods: 344 patients with COPD participating in our prospective cohort who had a chest CT were studied. Results: 245 patients had atelectasis reported in their chest CT scan. Atelectasis was associated with female sex (64 vs. 49%, p=0.01) and lower blood eosinophil counts (0.17±0.14 vs. 0.22±0.16, p=0.02). However, there was no difference between the two groups in age, BMI, lung function, exacerbations in the year before LT-NIV setup, smoking history, symptom scores or pre-setup blood gases. There was a significant decrease in the number of annualised exacerbations following LT-NIV setup (from 3.00 /2.00-5.75/ to 2.95 /1.28-5.00/, p=0.04). However, patients with atelectasis tend to experience a more modest decrease (from 3.00 /2.00-6.00/ to 3.00 /1.41-5.21/ vs. from 3.00 /2.00-5.50/ to 2.40 /0.95-4.67/, p=0.07). Similarly, patients with atelectasis tend to have a shorter exacerbation-free survival following LT-NIV setup (5 vs. 9 months median exacerbation-free survival, p=0.09). Most importantly, the overall survival was significantly lower in patients with atelectasis (60 vs. 77 months, median survival p=0.03). Conclusion: Atelectasis on chest CT scan is associated with worse clinical outcomes and may represent a treatable trait in patients with COPD.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".