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Record W4402345434 · doi:10.1016/j.ijscr.2024.110195

Malignant duodenal gastrointestinal neuroectodermal tumor (GNET): Case report and review of the literature

2024· article· en· W4402345434 on OpenAlexaff
Antony Fournier, Vicki Deslauriers, Charlie Champagne Giguère, Martin Borduas, Yves Collin

Bibliographic record

VenueInternational Journal of Surgery Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineGastrointestinal tractNeuroectodermal tumorMalignancyPathologyInternal medicineImmunohistochemistry

Abstract

fetched live from OpenAlex

INTRODUCTION: Malignant gastrointestinal neuroectodermal tumor (GNET) is a rare malignancy primarily affecting the gastrointestinal tract. Upon cross-sectional imaging, it can be easily confused with other mesenchymal tumors. This article presents a case of duodenal GNET and reviews the current literature on this rare entity. PRESENTATION OF CASE: A 73-year-old female patient presented with a 4 cm duodenal mass on CT scan. With a presumptive diagnosis of GIST, a D3-D4 duodenectomy with cholecystectomy were performed. Subsequent pathological analysis of the surgical specimen revealed a 4.5 cm malignant gastrointestinal neuroectodermal tumor (GNET), also known as clear cell sarcoma-like gastrointestinal tumor (CCSLGT). DISCUSSION: While there are less than 115 cases of GNET reported worldwide, prognosis is usually poor with a 50 % survival at 3 years, and mortality rate described is as high as 75 %. To the authors' knowledge, this duodenal GNET case represents the first one ever described for this location. CONCLUSION: Early recognition of GNET is essential due to its poor prognosis and its ability to metastasize. Awareness of its existence and diagnostic criteria by every member of the medical team is key to obtain optimal patient care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.069
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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