Multidimensional severity assessment in bronchiectasis: an analysis of seven European cohorts
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Bibliographic record
Abstract
INTRODUCTION: Bronchiectasis is a multidimensional disease associated with substantial morbidity and mortality. Two disease-specific clinical prediction tools have been developed, the Bronchiectasis Severity Index (BSI) and the FACED score, both of which stratify patients into severity risk categories to predict the probability of mortality. METHODS: We aimed to compare the predictive utility of BSI and FACED in assessing clinically relevant disease outcomes across seven European cohorts independent of their original validation studies. RESULTS: The combined cohorts totalled 1612. Pooled analysis showed that both scores had a good discriminatory predictive value for mortality (pooled area under the curve (AUC) 0.76, 95% CI 0.74 to 0.78 for both scores) with the BSI demonstrating a higher sensitivity (65% vs 28%) but lower specificity (70% vs 93%) compared with the FACED score. Calibration analysis suggested that the BSI performed consistently well across all cohorts, while FACED consistently overestimated mortality in 'severe' patients (pooled OR 0.33 (0.23 to 0.48), p<0.0001). The BSI accurately predicted hospitalisations (pooled AUC 0.82, 95% CI 0.78 to 0.84), exacerbations, quality of life (QoL) and respiratory symptoms across all risk categories. FACED had poor discrimination for hospital admissions (pooled AUC 0.65, 95% CI 0.63 to 0.67) with low sensitivity at 16% and did not consistently predict future risk of exacerbations, QoL or respiratory symptoms. No association was observed with FACED and 6 min walk distance (6MWD) or lung function decline. CONCLUSION: The BSI accurately predicts mortality, hospital admissions, exacerbations, QoL, respiratory symptoms, 6MWD and lung function decline in bronchiectasis, providing a clinically relevant evaluation of disease severity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it