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Record W4404410709 · doi:10.1093/bjd/ljae447

Exploring the genetic driver events of eccrine poromas and porocarcinomas: a retrospective, cross-institutional study of 54 cases

2024· article· en· W4404410709 on OpenAlexfundno aff
Mark J. Arends, Martin Del Castillo Velasco‐Herrera, Saamin Cheema, Kim Ping Wong, Jacqueline Marcia Boccacino, Ian Vermes, Kirsty Roberts, Elizabeth Anderson, Michiel P J van der Horst, Nicolas de Saint Aubain, Ahmed Al‐Omari, Carlos Monteagudo, Steven D. Billings, Derek Frew, Emily L. Clarke, William Merchant, Neil Rajan, Peter M. Ferguson, Carolin Mogler, Ingrid Ferreira, Thomas Brenn, Louise van der Weyden, David J. Adams

Bibliographic record

VenueBritish Journal of Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer and Skin Lesions
Canadian institutionsnot available
FundersMedical Research CouncilNational Institute for Health and Care ResearchMedical Research Council CanadaWellcome Trust
KeywordsCarcinogenesisSomatic cellFusion geneYAP1GeneBiologyMutationCancer researchGeneticsSomatic fusionTranscription factor

Abstract

fetched live from OpenAlex

We have comprehensively characterized the mutational landscape of eccrine poroma (EP) and eccrine porocarcinoma (EPC), uncovering novel molecular events and delineating different pathways of tumorigenesis underlying these tumours. EPs are driven largely by oncogenic fusion genes, whereas EPCs are driven largely by somatic mutations affecting various pathways, with a subset driven by fusion genes as the first oncogenic driver, and somatic mutations representing secondary events contributing to progression of the tumour. Fusion genes in EP predominantly involve YAP1, while those in EPC preferentially involve PAK gene family members.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.308
Teacher spread0.261 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

Explore more

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