MétaCan
Menu
Back to cohort
Record W2774071740 · doi:10.1177/1066896917747732

Epithelial Myoepithelial Carcinoma of the Nasal Cavity: Clinical, Histopathological, and Immunohistochemical Distinction of a Case Report

2017· article· en· W2774071740 on OpenAlexaff
Sally Nguyen, Marjorie Perron, Sylvie Nadeau, Alexandre Nakao Odashiro, Marie-Noëlle Corriveau

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2017
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsImmunohistochemistryNasal cavityMyoepithelial cellPathologyMedicineHistopathologyCarcinomaOral cavityAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: Epithelial myoepithelial carcinomas (EMCs) are rare low-grade salivary gland tumors. Here, we report the case of a 75-year-old man presenting with an oncocytic variant of EMC of the nasal cavity, initially diagnosed as an oncocytoma. METHODS: Our patient underwent functional sinus surgery in 2012. On pathology, an oncocytic neoplasm was found in the right nasal cavity, characterized by fragments of uniform bland oncocytic cells with bilayered arrangement of nuclei. Immunohistochemical stains demonstrated biphasic cells: luminal epithelial and basal cell-type myoepithelial cells. The tumor was best diagnosed as an oncocytoma. In 2015, the patient presented with a recurrent right inferior turbinate lesion, compatible with oncocytic EMC. RESULTS: The patient underwent oncological surgery and received adjuvant radiotherapy. He had no disease recurrence. CONCLUSION: Different variants of EMCs exist, such as oncocytic EMC. EMCs should be treated aggressively because they can be locally invasive, recur, and give rise to distant metastases.

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)0.002
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.365
Teacher spread0.326 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Surgical PathologySame topicSalivary Gland Tumors Diagnosis and TreatmentFrench-language works237,207