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Record W3116079627 · doi:10.14740/gr1323

Clinicopathologic and Molecular Features of Mixed Neuroendocrine Non-Neuroendocrine Neoplasms of the Gallbladder

2020· article· en· W3116079627 on OpenAlexvenueno aff
Mouyed Alawad, Raavi Gupta, M.A. Haseeb, F. Charles Brunicardi

Bibliographic record

VenueGastroenterology Research · 2020
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsGallbladderMedicineAdenocarcinomaPathologyNeoplasmCholecystectomyCarcinomaNeuroendocrine tumorsImmunohistochemistryNeuroendocrine carcinomaGastroenterologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Neuroendocrine neoplasms of the gallbladder are rare, comprising 0.5% of all neuroendocrine cancers and about 2% of gallbladder cancers. These neoplasms can also be found along with other malignant neoplasms of epithelial origin, mostly adenocarcinomas. Herein, we describe an unusual finding of a three-component mixed neuroendocrine non-neuroendocrine neoplasm (MiNEN) of the gallbladder. We also review the literature on 29 similar cases and summarize key features. We report on a 62-year-old woman who presented with right upper quadrant pain with a positive Murphy's sign. A clinical diagnosis of neoplasia was entertained and she underwent cholecystectomy. Gross examination of the specimen revealed a 5-cm exophytic mass at the gallbladder fundus. Histopathologic examination of the mass showed an infiltrating squamous cell carcinoma, an adjacent neuroendocrine carcinoma (each of these two components composed more than 30% of the neoplasm), and a superficial adenocarcinoma (composing 10% of the neoplasm). Gallbladder MiNENs present with similar symptoms and in the same age group as do carcinomas; however, their prognosis is often poor. Specific management and treatment guidelines have not been established since MiNENs are very rare.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.042
GPT teacher head0.356
Teacher spread0.313 · 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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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