Prognostic value of programmed death-ligand 1 in solid tumors: A meta-analysis.
Bibliographic record
Abstract
e19121 Background: The programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) pathway plays a crucial role in cancer immuno-surveillance and is the target of approved immunotherapeutic drugs. Available data suggest a variable prognostic impact of PD-L1 expression in solid tumors. Methods: A systematic literature search of electronic databases identified publications exploring the effect of PD-L1 on overall survival (OS) and/or progression-free survival (PFS). Hazard ratios (HR) were pooled in a meta-analysis using generic inverse-variance and random effects modeling. Subgroup analyses were conducted based on disease site, stage of disease, and method of PD-L1 quantification using the Deeks method. Results: One hundred eighty-eight studies comprised of 212,748 patients met the inclusion criteria. PD-L1 expression was associated with worse OS (HR 1.32, 95% confidence interval (CI) 1.25 - 1.38; P < 0.001). There was significant heterogeneity between disease sites (subgroup P = 0.002) with pancreatic, hepatocellular and genitourinary cancers being associated with the highest magnitude of adverse outcome (Table). PD-L1 was also associated with worse overall PFS (HR 1.19, 95% CI 1.09 - 1.30; P < 0.001). Stage of disease did not significantly affect the results (subgroup P = 0.52), nor did the method of quantification (immunohistochemistry or mRNA) (subgroup P = 0.70). Conclusions: High expression of PD-L1 is associated with worse cancer outcomes albeit with significant heterogeneity between disease sites. The effect seems consistent in early stage and metastatic disease and is not sensitive to method of PD-L1 quantification. [Table: see text]
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".