Pleural pro-gastrin releasing peptide is a potential diagnostic marker for malignant pleural effusion induced by small-cell lung cancer
Bibliographic record
Abstract
Background: Serum pro-gastrin releasing peptide (proGRP) is a well-recognized diagnostic marker for small cell lung cancer (SCLC). Pleural effusion is common in patients with advanced SCLC. The diagnostic accuracy of pleural proGRP for malignant pleural effusion (MPE) has not yet been established. This study aimed to evaluate the diagnostic accuracy of pleural proGRP for MPE. Methods: We prospectively recruited patients with undiagnosed pleural effusions from two centers (Hohhot and Changshu). An electrochemiluminescence immunoassay was used to detect pleural fluid proGRP. The diagnostic accuracy of proGRP for MPE was evaluated using a receiver operating characteristic (ROC) curve. Results: In both the Hohhot (n=153) and Changshu (n=58) cohorts, pleural proGRP in MPE patients did not significantly differ from that in patients with benign pleural effusions (BPEs) (Hohhot, P=0.91; Changshu, P=0.12). In the Hohhot and Changshu cohorts, the areas under the curves (AUCs) of proGRP were 0.51 [95% confidence interval (CI): 0.41-0.60] and 0.62 (95% CI: 0.47-0.77), respectively. However, patients with SCLC-induced MPE had significantly higher proGRP levels than those with BPE and other types of MPE (P=0.001 for both). In the pooled cohort, the AUC of proGRP for SCLC-induced MPE was 0.90 (95% CI: 0.78-1.00, P=0.001). At a threshold of 40 pg/mL, proGRP had a sensitivity of 1.00 (95% CI: 0.61-1.00) and specificity of 0.59 (95% CI: 0.52-0.66). The positive likelihood ratio was 2.61 (95% CI: 1.99-3.41), and the negative likelihood ratio was 0. Conclusions: Pleural proGRP has no diagnostic value for MPE, but has high diagnostic accuracy for SCLC-induced MPE. In patients with proGRP levels <40 pg/mL, MPE secondary to SCLC can be excluded.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".