Prognosis of Spontaneous Pneumothorax/Pneumomediastinum in Coronavirus Disease 2019: The CoBiF Score
Why this work is in the frame
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Bibliographic record
Abstract
Objectives: Pneumothorax and pneumomediastinum are associated with high mortality in invasively ventilated coronavirus disease 2019 (COVID-19) patients; however, the mortality rates among non-intubated patients remain unknown. We aimed to analyze the clinical features of COVID-19-associated pneumothorax/pneumomediastinum in non-intubated patients and identify risk factors for mortality. Methods: We searched PubMed Scopus and Embase from January 2020 to December 2021. We performed a pooled analysis of 151 patients with no invasive mechanical ventilation history from 17 case series and 87 case reports. Subsequently, we developed a novel scoring system to predict in-hospital mortality; the system was further validated in multinational cohorts from ten countries (n = 133). Results: Clinical scenarios included pneumothorax/pneumomediastinum at presentation (n = 68), pneumothorax/pneumomediastinum onset during hospitalization (n = 65), and pneumothorax/pneumomediastinum development after recent COVID-19 treatment (n = 18). Significant differences were not observed in clinical outcomes between patients with pneumomediastinum and pneumothorax (±pneumomediastinum). The overall mortality rate of pneumothorax/pneumomediastinum was 23.2%. Risk factor analysis revealed that comorbidities bilateral pneumothorax and fever at pneumothorax/pneumomediastinum presentation were predictors for mortality. In the new scoring system, i.e., the CoBiF system, the area under the curve which was used to assess the predictability of mortality was 0.887. External validation results were also promising (area under the curve: 0.709). Conclusions: The presence of comorbidity bilateral pneumothorax and fever on presentation are significantly associated with poor prognosis in COVID-19 patients with spontaneous pneumothorax/pneumomediastinum. The CoBiF score can predict mortality in clinical settings as well as simplify the identification and appropriate management of patients at high risk.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it