PD-L1 expression complements CALGB prognostic scoring system in malignant pleural mesothelioma
Bibliographic record
Abstract
Background Programmed death ligand-1 (PD-L1) expression is a predictive biomarker in patients with lung cancer, but its role in malignant pleural mesothelioma (MPM) remains unclear. Evidence suggests that higher PD-L1 expression is correlated with worse survival. CALGB is the main scoring system used to predict the benefit of chemotherapy treatment. This study aimed to determine the prognostic value of PD-L1 expression and its addition to CALGB scoring system in patients with MPM. Methods In this retrospective analysis, we evaluated samples with confirmed locally advanced or metastatic MPM. PD-L1 Tumor Proportional Score (TPS) was determined by immunohistochemistry at diagnosis. Results 73 patients were included in this study. A cutoff value of 15 was set for a high or low PD-L1 TPS. In total, 71.2% (n=52) and 28.8% (n=21) of individuals harbored low or high PD-L1 expression, respectively. PD-L1 High was associated with worse median progression-free Survival (mPFS) [4.9 vs. 10.8 months; HR 2.724, 95% CI (1.44-5.14); p = 0.002] and Overall Survival (OS) [6.0 vs. 20.9 months; HR 6.87, 95% CI (3.4-8.7); p<0.001] compared to patients with PD-L1 Low . Multivariate analysis confirmed that PD-L1 expression was an independent factor for PFS and OS in patients with MPM and CALGB score of 5-6. Conclusion PD-L1 addition to CALGB scale improves its prognostic estimation of MPM survival and should be considered in future research.
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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 | 0.001 |
| 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".