Bibliographic record
Abstract
With the exception of well-differentiated liposarcoma, dedifferentiated liposarcoma, leiomyosarcoma, solitary fibrous tumour, malignant peripheral nerve sheath tumour, and undifferentiated pleomorphic sarcoma, the majority of the ≈70 histologic subtypes of retroperitoneal sarcoma are defined as 'ultra-rare' sarcomas, with an incidence of ≤1-5/1,000,000 persons/year.For most of these ultra-rare RPS subtypes, diagnosis and treatment follows international guidelines for the management of more common RPS histologies, with en bloc surgical resection as the mainstay of curative treatment, and enrolment in clinical trials where possible.Because the treatment of RPS is heavily driven by histology, the surgeon must be familiar with specific issues related to the diagnosis and management of ultra-rare sarcoma subtypes.Expert radiological and surgeon reviews are required to differentiate similarly presenting tumours where surgery can be avoided (e.g., angiomyolipoma), or where upfront systemic therapy is indicated (e.g., extraosseous Ewing's sarcoma).Thus, the management of all retroperitoneal sarcomas should occur at a sarcoma referral centre, with a multidisciplinary team of experts dedicated to the surgical and medical management of these rare tumours.In this focused review, we highlight how diagnosis and management of the ultra-rare primary RPS histologies of malignant perivascular epithelioid cell tumour (PEComa), extraosseous Ewing sarcoma (EES), extraosseous osteosarcoma (EOS), and rhabdomyosarcoma (RMS) critically diverge from the management of more common RPS subtypes.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.391 | 0.300 |
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".