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Record W3038346106 · doi:10.1159/000506296

Association between PD-L1 Expression and the Prognosis and Clinicopathologic Features of Renal Cell Carcinoma: A Systematic Review and Meta-Analysis

2020· review· en· W3038346106 on OpenAlexaboutno aff
Maolei Shen, Guang Chen, Qiang Xie, Xin Li, Hao Xu, Hui Wang, Shankun Zhao

Bibliographic record

VenueUrologia Internationalis · 2020
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHazard ratioRenal cell carcinomaConfidence intervalMeta-analysisInternal medicineCochrane LibrarySubgroup analysisGastroenterologyCarcinomaOncology

Abstract

fetched live from OpenAlex

The expression of programmed cell death-ligand 1 (PD-L1) and its correlation with the prognosis and clinicopathologic features of renal cell carcinoma (RCC) remain controversial to date. Concerning this issue, we had conducted a meta-analysis of relevant studies searched in the Web of Science, PubMed, EMBASE, and Cochrane Library databases. The Newcastle-Ottawa quality assessment scale was applied to assess the quality of the included studies. The hazard ratio (HR) and its corresponding 95% confidence intervals (CIs) were collected by Stata 12.0 and used for the results of overall survival (OS) and disease-free survival (DFS). A total of 1,644 patients in 8 studies were included in this meta-analysis. Results showed that PD-L1 expression significantly correlated with OS (HR = 1.98, 95% CI: 1.22-3.22, Z = 2.77, p = 0.006) and DFS (HR = 3.70, 95% CI: 2.07-6.62, Z = 4.40, p = 0.0001) in ccRCC. Subgroup analysis indicated that PD-L1 expression significantly correlated with the lymph-gland transfer ratio (HR = 2.45, 95% CI: 1.02-5.92, Z = 1.99, p = 0.05) and tumor necrosis (HR = 6.05, 95% CI: 3.78-9.67, Z = 7.51, p < 0.00001). This meta-analysis suggests that PD-L1 expression is a valuable prognostic tool for patients with ccRCC. Subgroup analyses demonstrated that it was helpful for screening patients with RCC who need anti-PD-1/PD-L1 treatment and support them to benefit from such immune-targeted therapy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.345
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2020
Admission routes1
Has abstractyes

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