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Record W4286268764 · doi:10.1016/j.ijscr.2022.107444

Case report of an angiosarcoma of the abdominal wall during liraglutide injections

2022· article· en· W4286268764 on OpenAlexaff
Éric Bergeron, Meriame Dami, Xuan Vien, Chantal Vallee, Jonathan Noujaim

Bibliographic record

VenueInternational Journal of Surgery Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicVascular Tumors and Angiosarcomas
Canadian institutionsHôpital Maisonneuve-RosemontHôpital Charles-Le Moyne
Fundersnot available
KeywordsMedicineAngiosarcomaLiraglutideHemangiosarcomaAbdominal wallCancerSurgeryType 2 diabetesDiabetes mellitusInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

ABSTRACT Introduction: Angiosarcoma is a very rare but highly aggressive malignant vascular tumor. Sporadic cases that develop in the abdominal wall are almost exclusively observed in obese patients. The underlying causes remain unclear. Liraglutide, a glucagon-like peptid-1 agonist receptor treatment, effective for weight management and glycemic control in type 2 diabetes, is not known to be associated with the occurrence of angiosarcoma. Case presentation: A 62-year-old woman developed a very aggressive, rapidly recurrent angiosarcoma of the abdominal wall. The angiosarcoma developed while she was taking liraglutide injections. The patient died within few months. Discussion: Angiosarcoma, particularly of the abdominal wall, is very rare, and occurs mainly in obese patients. This cancer had a highly aggressive behavior. The association with liraglutide and angiosarcoma cannot be established nor eliminated from this case and literature review. Conclusion: Angiosarcoma is a rare and highly aggressive cancer that occasionally originates in the abdominal wall. In this case, in addition to the underlying obesity, a possible association of her liraglutide subcutaneous injections cannot be excluded. In the future, if other cases of abdominal walls angiosarcomas associated with liraglutide subcutaneous injections were to be reported, a possible causality should be further investigated. Highlights

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)0.002
metaresearch head score (Gemma)0.000
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 score0.047
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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