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Record W2756507686 · doi:10.1016/j.ijscr.2017.09.017

Case report of a littoral cell angioma of the spleen and accessory spleens

2017· article· en· W2756507686 on OpenAlexaff
Yagan Pillay, M. Omar Shokeir

Bibliographic record

VenueInternational Journal of Surgery Case Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicAbdominal Trauma and Injuries
Canadian institutionsPrince Albert Grand Council
Fundersnot available
KeywordsMedicineCD68SpleenMagnetic resonance imagingAngiomaPathologySplenectomyHemangiomaCD31Vascular tumorRadiologyVascular diseaseSurgeryImmunohistochemistryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Littoral- cell angioma (LCA) is a rare benign vascular tumour of the spleen. There have been less then 80 cases reported in the literature. Recent reports have described it to be a malignant lesion with congenital and immunologic associations. We report a case of LCA of the spleen. PRESENTATION OF CASE: A 52 -year-old male patient was admitted to hospital with a three month duration of intermittent upper abdominal pain and nausea. Imaging studies, including computer tomography (CT) and magnetic resonance imaging (MRI), showed multiple lesions in the spleen as well as in the accessory spleens. An open splenectomy was performed and his post-operative recovery was uneventful. DISCUSSION: Littoral cell angioma of the spleen is a benign vascular tumour that has been infrequently reported in the English literature. While it does have malignant potential, the vast majority are benign. Diagnosis depends on the expression of endothelial markers like CD31 and histiocytic markers like CD68.Malignant potential is enhanced by the presence of splenomegaly as well. CONCLUSION: This rare condition is made even more rare by the presence of the tumour in the two accessory spleens as well.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0030.003
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0050.002

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 head0.064
GPT teacher head0.344
Teacher spread0.280 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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