Eosinophils, exacerbation history, and BMI as significant predictors of acute COPD exacerbations?
Bibliographic record
Abstract
Introduction: Identifying early predictors of acute exacerbations of COPD (AECOPD) is essential for improving preventive strategies and tailoring clinical management. This study was designed to identify clinical, functional, and biomarker-based predictors of AECOPD. Methods: This trial was a monocentric, prospective observational study recruiting a total of 355 patients with COPD (GOLD II–IV) during a 3-week inpatient pulmonary rehabilitation program where patients were closely monitored for AECOPD events. Baseline assessments included lung and cardiovascular evaluations, exercise capacity, measurements of systemic biomarkers and others. Results: 57 out of 355 subjects (16%) developed a physician-diagnosed AECOPD in the observational phase. The statistical analysis of 165 variables involved univariable analyses of all predictors with significant effects, followed by stepwise variable selection, and final model fitting on a dataset with complete cases, identifying six predictors, three of which showed a significant association with the AECOPD risk: blood eosinophil count (p<0.001), number of previous AECOPD hospitalizations (p=0.002), and body-mass-index (p=0.019). Conclusion: This study identified systemic inflammation, exacerbation history, and nutritional status as strong risk factors for AECOPD. Further research is warranted to explore their potential for guiding personalized preventive interventions. erj;66/suppl_69/PA438/F1 F1 F1
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".