Association between PD-L1 expression and head and neck cancer prognosis: a meta-analysis
Bibliographic record
Abstract
Abstract Background: Programmed cell death ligand 1（PD-L1) plays an important role in tumor cell immune escape, and it has been extensively studied in head and neck cancer. However, its prognostic impact on patients with head and neck cancer remains controversial, so we sought to investigate this issue through a comprehensive meta-analysis. Methods: To assess the significance of PD-L1 on the survival of patients with head and neck cancer, we collected articles reported in PubMed, EMBASE, and Cochrane Library, until January 31, 2019. We also used the Newcastle Ottawa Scale (NOS) for literature quality evaluation. Results: The study included a total of 4551 patients affected by 6 different types of head and neck cancer reported in 26 articles. Our study found that the association between the expression of PD-L1 and the prognosis of head and neck tumors was highly heterogeneous (P < 0.00001, I2 = 80.0%); therefore, the random effects model was applied to combine the effect sizes. Based on the combined hazard ratios (HR)of 1.15 (95% CI: 0.88 to 1.50, P = 0.32), the expression of PD-L1 in head and neck tumors may not be a factor associated with poor prognosis. Conclusions: Our results suggest that PD-L1 expression cannot predict the overall survival of patients with oral, nasopharyngeal, or esophageal cancer. Through subgroup analysis, we found that the expression of PD-L1 may be a poor prognostic factor for some head and neck cancers.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".