Organizing Pneumonia Pattern on Chest CT: Prevalence and Association with Clinical Outcomes in a Cohort of Patients with Severe/Critical COVID-19
Bibliographic record
Abstract
Introduction: COVID-19 pneumonia can present with two distinct radiologic patterns: diffuse alveolar damage or organizing pneumonia. These patterns have been linked to different outcomes in non-COVID-19 settings. We sought to assess the prevalence of organizing pneumonia radiologic pattern and its association with clinical outcomes. Methods: We performed a retrospective cohort study including adult patients hospitalized for severe/critical COVID-19 who underwent chest computed tomography within 21 days of diagnosis. Radiologic patterns were reviewed and classified by two expert radiologists. Results: Among 80 patients included, 89% (n=71) presented a pattern consistent with organizing pneumonia. The main radiologic findings were multilobar (98.7%) and bilateral (97.6%) distribution with ground glass opacities (97.6%). Intensive care admission was required for 44% (n=33) of subjects, of which 24% (n=19) received mechanical ventilation. The presence of organizing pneumonia was independently associated with a decreased odds of mechanical ventilation or death (Odds ratio 0.14; 95% confidence interval 0.02 - 0.96; p value 0.045) in a multivariate model including age, gender, BMI and lung involvement on CT. Conclusion: A radiologic pattern of organizing pneumonia is highly prevalent in patients with severe/critical COVID-19 and is associated with improved clinical outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".