Case report of an angiosarcoma of the abdominal wall during liraglutide injections
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".