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Record W4377107883 · doi:10.3390/curroncol30050388

PD-L1 Expression in Cutaneous Angiosarcomas: A Systematic Review with Meta-Analysis

2023· review· en· W4377107883 on OpenAlexvenueno aff
Renato Lobrano, Panagiotis Paliogiannis, Angelo Zinellu, Giuseppe Palmieri, Ivana Persico, Arduino A. Mangoni, Antonio Cossu

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicVascular Tumors and Angiosarcomas
Canadian institutionsnot available
FundersUniversità degli Studi di Sassari
KeywordsMedicineMeta-analysisAngiosarcomaWeb of scienceImmunohistochemistryInternal medicineScopusOncologyMEDLINEPathologyBiology

Abstract

fetched live from OpenAlex

Cutaneous angiosarcoma (CAS) is the most common type of angiosarcoma that predominantly affects older Caucasians. The outcomes of immunotherapy in CAS are currently under investigation in relation to the expression of programmed death ligand 1 (PD-L1) and other biomarkers. We performed a systematic review and metanalysis of data from the current literature reporting on PD-L1 immunohistochemistry expression. A systematic search of publications in the electronic databases PubMed, Web of Science, and Scopus was conducted using the following terms: “PD-L1” and “angiosarcomas”. A total of ten studies reporting on 279 cases were identified and included in the meta-analysis. The pooled prevalence of PD-L1 expression in CAS was 54% (95% CI 36–71%), with high heterogeneity (I2 = 84.81%, p < 0.001). In sub-group analysis, the proportion of PD-L1 expression in CAS was significantly (p = 0.049) lower in Asian studies (ES = 35%, 95% CI 28–42%, I2 = 0.0%, p = 0.46) than in European studies (ES = 71%, 95% CI 51–89%, I2 = 48.91%, p = 0.12).

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.024
Bibliometrics0.0060.008
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.310
GPT teacher head0.470
Teacher spread0.160 · 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.

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

Citations7
Published2023
Admission routes1
Has abstractyes

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