Late Breaking Abstract - Eosinophillic pleural effusion: etiology, management and outcomes – data from the International Multicentre Pleural Research Collaborative
Bibliographic record
Abstract
Introduction: Data from large multicentre studies on the etiology, management and outcome of eosinophilic pleural effusion (EPE) are lacking. Aim: To evaluate the etiology, characteristics, underlying causes, and outcomes of EPE in a diverse international patient cohort. Methods: Anonymous data of 226 EPE patients were collected across 7 countries, as part of an ERS clinical research collaboration (International Multicentre Pleural Research Collaborative). Results: 210 cases were finally analysed (144 men; median age 66.5 (IQR 55-78) years; median eosinophil proportion in pleural fluid 22% (14-43 %). The most common causes of EPE were malignancy (27.6%), infections (20.4%), post-traumatic events (7.6%), and drug-related reactions (6.2%). 21.9% of cases were classified as EPE of unknown etiology. Median number of investigations needed for diagnosis was 2 (2-3). EPE was managed with drainage (31.9%), repeated thoracentesis (27.1%), specific medications (15.2%), pleurodesis (8.5%) and IPC (3.8%). In 50.7% of patients EPE resolved within a median time of 2 (1-4.5) months. Malignant EPE affected older patients (p=0.0003), was more frequently associated with smoking history (p=0.0004) and pleuritic chest pain (p=0.025) than non-malignant EPE. The percentage of eosinophils in malignant EPE was significantly lower compared to non-malignant EPE (median 18 (13-30)% vs 24 (14-46), p=0.027). Smoking history was a risk factor of malignant etiology in EPE (OR 4.8, 95% CI 1.4 to 16.3). Conclusions: This is the first multicentre study providing comprehensive data on the diverse etiology, characteristics, and outcomes of EPE in a large, non-selected patient cohort
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".