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Record W2292279456 · doi:10.1002/dc.23399

Fine‐needle aspiration of soft tissue myoepithelioma

2015· article· en· W2292279456 on OpenAlexaff
Gang Wang, Tracy Tucker, Tony Ng, Carlos F. Villamil, Malcolm Hayes

Bibliographic record

VenueDiagnostic Cytopathology · 2015
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsVancouver General HospitalBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsMyoepitheliomaMyoepithelial cellPathologyCytokeratinFine-needle aspirationFluorescence in situ hybridizationSoft tissueMedicineBiopsyVimentinEosinophilicAnatomyBiologyImmunohistochemistry

Abstract

fetched live from OpenAlex

Soft tissue myoepithelioma is a rare neoplasm composed of myoepithelial cells. We describe the cytologic features of a soft tissue myoepithelioma arising in the right lower chest wall in a 65-year-old woman. The fine-needle aspiration (FNA) smears showed round to oval, spindle, epithelioid, and plasmacytoid cells in the myxoid background. The nuclei were uniform, round to ovoid, with finely distributed chromatin and eosinophilic or pale cytoplasm, and resembled lobular carcinoma of breast. Ultrasound guided core biopsy showed the tumor cells had bland cytologic features, arranged in small cords, nests, and dissociated single cells, with no glandular differentiation or breast tissue seen. The tumor cells demonstrated immunoreactivity for cytokeratin (AE1/AE3) and glial fibrillary acidic protein, but were negative for estrogen receptor. Fluorescence in situ hybridization demonstrated the EWSR1 rearrangement, confirming the diagnosis of myoepithelioma.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.038
GPT teacher head0.303
Teacher spread0.265 · 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 designCase report
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

Citations18
Published2015
Admission routes1
Has abstractyes

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