Apolipoprotein E in patients with undiagnosed pleural effusion: a prospective diagnostic test accuracy study
Bibliographic record
Abstract
INTRODUCTION: Pleural effusion is common in clinical practice, and its differential diagnosis remains challenging for clinicians. This study investigates the diagnostic value of apolipoprotein E (apoE) in patients with undetermined pleural effusion. METHODS: This prospective, double-blind study enrolled 152 patients with undiagnosed pleural effusion. Their pleural fluid apoE levels were measured, and a receiver operating characteristics (ROC) curve was used to evaluate the diagnostic accuracy of apoE. Decision curve analysis (DCA) was used to assess apoE's net benefit. Subgroup analyses were performed to investigate the effect of age on the diagnostic accuracy of apoE. RESULTS: Among the included participants, 23 had heart failure (HF). HF patients had the lowest apoE level among pleural effusion patients. The area under the curve (AUC) of apoE for HF was 0.79 (95% CI: 0.69-0.89). At the threshold of 40 mg/L, the sensitivity and specificity of apoE were 0.96 (95% CI: 0.87-1.00) and 0.33 (95% CI: 0.25-0.42), respectively. The decision curve for apoE was above reference lines. The AUC of apoE decreased in older patients. CONCLUSION: Pleural fluid apoE has moderate diagnostic value for HF and has net benefits in patients with undiagnosed pleural effusion. The diagnostic accuracy of apoE decreases with age.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".