Investigation and outcomes in patients with nonspecific pleuritis: results from the International Collaborative Effusion database
Bibliographic record
Abstract
Introduction We present findings from the International Collaborative Effusion database, a European Respiratory Society clinical research collaboration. Nonspecific pleuritis (NSP) is a broad term that describes chronic pleural inflammation. Various aetiologies lead to NSP, which poses a diagnostic challenge for clinicians. A significant proportion of patients with this finding eventually develop a malignant diagnosis. Methods 12 sites across nine countries contributed anonymised data on 187 patients. 175 records were suitable for analysis. Results The commonest aetiology for NSP was recorded as idiopathic (80 out of 175, 44%). This was followed by pleural infection (15%), benign asbestos disease (12%), malignancy (6%) and cardiac failure (6%). The malignant diagnoses were predominantly mesothelioma (six out of 175, 3.4%) and lung adenocarcinoma (four out of 175, 2.3%). The median time to malignant diagnosis was 12.2 months (range 0.8–32 months). There was a signal towards greater asbestos exposure in the malignant NSP group compared to the benign group (0.63 versus 0.27, p=0.07). Neither recurrence of effusion requiring further therapeutic intervention nor initial biopsy approach were associated with a false-negative biopsy. A computed tomography finding of a mass lesion was the only imaging feature to demonstrate a significant association (0.18 versus 0.01, p=0.02), although sonographic pleural thickening also suggested an association (0.27 versus 0.09, p=0.09). Discussion This is the first multicentre study of NSP and its associated outcomes. While some of our findings are reflected by the established body of literature, other findings have highlighted important areas for future research, not previously studied in NSP.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".