Primary Angiosarcoma of the Spleen: An Aggressive Neoplasm
Bibliographic record
Abstract
Primary tumors of the spleen are rare, with an incidence rate of about 0.1%. These tumors could be benign, usually asymptomatic, or malignant which are usually symptomatic with abdominal pain being the most common symptom. Lymphoid neoplasms are the most common primary splenic tumors. Primary angiosarcoma is one of the extremely rare malignant vascular neoplasms of the spleen, which carries a dismal prognosis. It constitutes almost 7.4% of all primary malignant splenic neoplasms and is well known as an aggressive tumor with high local recurrence and distant metastasis rates. Overall survival is up to 12 months following diagnosis, regardless of management strategy. Due to the broad differential diagnosis of splenic tumors, this tumor is often forgotten, and is very challenging to diagnose early. Less than 300 cases of primary splenic angiosarcoma have been reported in the English literature. The main issue of this article is to review the current English literature to figure out the characteristic demographic features, clinical presentation, imaging findings and management of such tumors, in order to increase awareness of the treating physicians to improve diagnosis, management, as well as overall survival.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".