Giant Retroperitoneal Liposarcoma: Correlation Between Size and Risk for Recurrence
Bibliographic record
Abstract
Soft tissue sarcomas (STSs) are rare tumors that represent almost 1% of adult malignant tumors. The annual incidence rate for such tumors is 2 - 5/100,000 population. The most common type of STS in adults is liposarcoma, which represents 15-20% of adult STSs. It is of mesodermic origin derived from adipose tissues, and known as the most common primary malignant tumor of the retroperitoneum. Other sites of involvement include the extremities, trunk and to a lesser extent the pleural cavity, esophagus, mediastinum and others. Due to the potential large retroperitoneal space, retroperitoneal liposarcoma (RPL) is usually asymptomatic during the initial phase, developing symptoms at a late stage due to large mass compressing nearby retroperitoneal structures. The average diameter and weight of RPL during diagnosis is 20 - 25 cm and 15 - 20 kg, respectively. Several factors were labelled as risk factors for recurrence, such as histological type, tumor grade, age, resectability and tumor size. Controversy exists regarding the relationship between tumor size and recurrence rate, thus, tumor size as a risk factor for recurrence should be clarified. Although there is no consensus regarding the precise definition of giant RPL, it is defined by several literatures as an RPL of greater than 30 cm in diameter or with weight of more than 20 kg. The main purpose of this article is to review the current English literature regarding giant RPL and examine the relationship between tumor size and risk for recurrence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".