Influences of age and sex on the diagnostic accuracy of human epididymis secretory protein 4 for malignant pleural effusion
Bibliographic record
Abstract
Previous studies have suggested that the presence of human epididymal protein 4 (HE4) in pleural fluid can be used to diagnose malignant pleural effusion (MPE) with moderate accuracy. However, the factors that affect the diagnostic accuracy of HE4 remain unknown. This study aimed to examine how age and sex influence the diagnostic accuracy of HE4. Participants with undiagnosed pleural effusion were prospectively enrolled in two cohorts (Hohhot cohort and Changshu cohort), and the presence of HE4 in their pleural fluid upon admission was determined by an electrochemiluminescence immunoassay. A receiver operating characteristic (ROC) curve with its area under the curve (AUC) was utilized to assess the diagnostic value of HE4 for MPE. Additionally, we conducted subgroup analyses and used a resampling method with different upper age limits to investigate the impacts of age and sex on the diagnostic accuracy of HE4 for MPE. The Hohhot cohort included 86 patients with benign pleural effusions (BPEs) and 66 patients with MPE, whereas the Changshu cohort included 26 patients with MPE and 32 patients with BPE. The diagnostic accuracy of HE4 decreased as age increased in both cohorts. The diagnostic accuracy of HE4 in males did not differ significantly from that in females. Therefore, we conclude that age should be considered when using HE4 in pleural fluid to diagnose MPE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".