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Record W3093642357 · doi:10.1177/1093526620968807

Adipocyte-rich <i>CTNNB1</i> -mutated Intramuscular Gardner Fibroma Progressing to Desmoid Fibromatosis

2020· article· en· W3093642357 on OpenAlexaff
Andrea Bakker, Jonathan C. Slack, Mara Caragea, Kyle C. Kurek, Marie‐Anne Bründler

Bibliographic record

VenuePediatric and Developmental Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFibromatosisImmunohistochemistryAggressive fibromatosisBiopsyPathologyPathogenesisFibromaIntramuscular fatBiologySoft tissueMedicine

Abstract

fetched live from OpenAlex

Gardner fibroma (GF) is a benign soft-tissue tumor that is associated with Gardner syndrome and can progress to, or co-occur with, desmoid fibromatosis (DF). Herein, we report a unique case of an 11-year-old boy who presented with a rapidly growing soft-tissue mass after biopsy of a stable fat-rich lesion present in the calf muscles since infancy, with Magnetic resonance imaging findings suggesting an intramuscular adipocytic tumor. The resection showed GF and DF. DF arising from a preexisting GF (the so-called “GF-DF sequence”) is a well-documented phenomenon. Although immunohistochemistry was negative for nuclear β-catenin expression, a CTTNB1 S45F mutation, which has been associated with aggressive behavior in DF, was identified in both components using a next-generation sequencing-based molecular assay. This is the first time a mutation in CTNNB1 has been identified in GF and the GF–DF sequence, thus expanding our knowledge of the molecular pathogenesis of the GF–DF sequence and highlighting the role of molecular testing in pediatric soft-tissue tumors. The histologic findings of an adipocyte-rich intramuscular GF also are unique, expanding the morphological spectrum of GF and adding GF to the differential diagnosis of intramuscular lesions with an adipocytic component.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.250
Teacher spread0.231 · 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.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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