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Record W4394552120 · doi:10.6084/m9.figshare.12608093

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

2020· review· en· W4394552120 on OpenAlexaboutno aff
Minhe Shen, Gui-Hai Chen, Q. Xie, Xiaogang Li, Hui Xu, Han Wang, Shu-mei Zhao

Bibliographic record

VenueFigshare · 2020
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisRenal cell carcinomaAssociation (psychology)MedicineOncologyExpression (computer science)Internal medicinePsychologyComputer sciencePsychotherapist

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score0.592

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.009
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.5850.024

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 head0.125
GPT teacher head0.347
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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