Pleural fluid C-C class chemokines 22 and pleural effusion due to heart failure: a prospective and double-blind diagnostic accuracy test
Bibliographic record
Abstract
BACKGROUND: Previous studies have indicated that C-C class chemokine ligand 22 (CCL22) is involved in the pathogenesis of tuberculous pleural effusion and malignant pleural effusion. However, the diagnostic role of pleural fluid CCL22 levels in patients with undiagnosed pleural effusions remains to be elucidated. METHODS: We prospectively recruited patients with undiagnosed pleural effusion who visited two centres (Hohhot and Changshu) in China. Pleural biopsy, microbiological culture and effusion cytology were used to verify the cause of pleural effusion. Pleural fluid CCL22 levels were measured using an ELISA. The diagnostic accuracy of CCL22 for identifying heart failure (HF) was evaluated using a receiver operating characteristic (ROC) curve, and the net benefit of CCL22 was evaluated using decision curve analysis (DCA). Net benefit was defined as the benefit associated with true positives minus the harms associated with false positives at various threshold probabilities. RESULTS: We enrolled 153 and 58 patients in the Hohhot and Changshu cohorts, respectively. The cohort included 28 patients with HF and 183 patients with non-HF. Patients with HF had significantly lower pleural fluid CCL22 levels than non-HF patients. The area under the ROC curve (AUC) of CCL22 was 0.85 (95% CI: 0.77 to 0.93) in the Hohhot cohort and 0.87 (95% CI: 0.75 to 0.98) in the Changshu cohort. The AUC in the combined cohort was 0.85 (95% CI: 0.79 to 0.92), with a sensitivity of 0.82 (95% CI: 0.68 to 0.93) and a specificity of 0.73 (95% CI: 0.67 to 0.79) at the threshold of 150 ng/mL. DCA revealed a potential net benefit of pleural CCL22 determination in patients with undiagnosed pleural effusions. CONCLUSIONS: Pleural fluid CCL22 may be a potential diagnostic marker for HF-related pleural effusion. Owing to the small sample size of this study, further studies with larger sample sizes are needed to validate our findings.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".