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Record W2473288363 · doi:10.14740/wjon926w

Malignant Retroperitoneal Extra-Gastrointestinal Stromal Tumor: A Unique Entity

2016· article· en· W2473288363 on OpenAlexvenueno aff
Shalini Thapar Laroia, Taruna Yadav, Archana Rastogi, Shiv Kumar Sarin

Bibliographic record

VenueWorld Journal of Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Tumor Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmunohistochemistryGastrointestinal tractStromal cellGiSTMesenteryPathologyStromal tumorRadiologyPresentation (obstetrics)Soft tissueInternal medicine

Abstract

fetched live from OpenAlex

Extra-gastrointestinal stromal tumors (EGISTs) are a recently described group of tumors. A handful of less than 70 cases have been reported in English literature, so far, to the best of our knowledge. Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the alimentary canal. EGISTs are a unique entity, which require distinction from GISTs because, even though, they exhibit similar histology and immunohistochemistry to GISTs, they occur outside the gastrointestinal tract, i.e. in omentum, mesentery, retroperitoneum, etc. and have different behavior patterns as far as their prognosis and management are concerned. Retroperitoneal sub-group of EGISTs is extremely rare and we report such a case of primary malignant EGIST of the retroperitoneum which presented as a soft tissue mass on radiological evaluation. The tumor turned out to be a histopathological surprise, and could be distinctively labeled as EGIST only after morphological and immunohistochemical studies. It is imperative for radiologists, pathologists and oncologists, among other clinicians, to be able to recognize and understand the presentation of this group of tumors due to their rapid progression and poor prognosis, so that an early diagnosis and management may be able to improve the final disease outcome.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.328
Teacher spread0.295 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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