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Record W3016161450 · doi:10.1002/dc.24431

Metastatic adenoid cystic carcinoma with high‐grade transformation (“dedifferentiation”) in pleural effusion and neck lymph node: A diagnostic challenge on cytology?

2020· article· en· W3016161450 on OpenAlexaff
Marc Pusztaszeri, Victor Brochu

Bibliographic record

VenueDiagnostic Cytopathology · 2020
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineAdenoid cystic carcinomaPathologyLymph nodeMyoepithelial cellPopulationPleomorphism (cytology)Pleural effusionCarcinomaImmunohistochemistryRadiology

Abstract

fetched live from OpenAlex

High-grade transformation (HGT) or "dedifferentiation" is an uncommon phenomenon among salivary gland carcinomas including adenoid cystic carcinoma (ACC), which is important to recognize because it is associated with increased tumor aggressiveness, with a high propensity for lymph node and distant metastases. ACC with HGT is histologically characterized by a distinct population of poorly differentiated cells with loss of the typical biphasic ductal and myoepithelial differentiation seen in conventional ACC, associated with pleomorphism, necrosis and increased mitotic activity. We report the cytologic features of a case of metastatic ACC-HGT in cervical lymph node and effusion, which, to the best of our knowledge, have not been described previously. When ACC presents both in atypical locations and with HGT, the danger of misdiagnosis is increased if the clinical history is lacking, incomplete or inaccurate. Since ACC-HGT are rare (and possibly underdiagnosed) and do not have a specific set of cytological and/or immunohistochemical features, it is important for practicing cytopathologists to be aware of the possibility of encountering them, especially in specimens from patients with a history of ACC, in order to render the correct diagnosis.

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)0.002
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.247
Teacher spread0.227 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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