Prognostic value of programmed cell death ligand 1 expression in patients with head and neck cancer: A systematic review and meta-analysis
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Programmed cell death ligand 1 (PD-L1) expression was reported to be correlated with poor prognosis in various cancers. However, the relationship between PD-L1 expression and the survival of patients with head and neck cancer (HNC) remains inconclusive. In the present study, we aimed to clarify the prognostic value of PD-L1 in HNC patients using meta-analysis techniques. METHODS: A comprehensive database searching was conducted in the PubMed, EMBASE, Web of Science and Cochrane Library from inception to August 2016. Studies meeting the inclusion criteria were included. The methodological quality of included studies was assessed by the Newcastle-Ottawa quality assessment scale. Hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) were pooled by STATA 11.0 for the outcome of overall survival (OS) and disease-free survival (DFS). RESULTS: A total of 17 studies with 2,869 HNC patients were included in the meta-analysis. The results of meta-analysis showed that there was no significant correlation between PD-L1 expression and OS (HR, 1.23; 95% CI, 0.99-1.53; P = 0.065) or DFS (HR, 1.42; 95% CI, 1.00-2.03; P = 0.052) of HNC patients. However, the subgroup analysis suggested that positive expression of PD-L1 was associated with poor OS (HR, 1.38; 95% CI, 1.12, 1.70; P = 0.003) and DFS (HR, 1.99; 95% CI, 1.59, 2.48; P = 0.001) in HNC patients from Asian countries/regions. The subgroup analysis also showed that the correlations between PD-L1 and prognosis are variant among different subtypes of HNC. When performing sensitive analyses, we found that the results of meta-analyses were not robust. CONCLUSION: The meta-analysis indicated that positive expression of PD-L1 could serve as a good predictor for poor prognosis of Asian patients with HNC. However, the findings still need to be confirmed by large-scale, prospective studies.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".