Low-grade fibromyxoid sarcoma and sclerosing epithelioid fibrosarcoma, outcome of advanced disease: retrospective study from the Ultra-Rare Sarcoma Working Group
Bibliographic record
Abstract
BACKGROUND: To present findings from a retrospective study conducted by the Ultra-Rare Sarcoma Working Group on metastatic low-grade fibromyxoid sarcoma (LGFMS), sclerosing epithelioid fibrosarcoma (SEF), and hybrid (H)-LGFMS/SEF across 28 global centres. METHODS: Patients treated at participating institutions from January 2000 to September 2022 were retrospectively selected. Diagnosis was confirmed by expert pathologists. Primary endpoint was progression-free survival (PFS-1) from metastasis detection to first progression or death. PFS-2 was calculated from therapy initiation. RESULTS: A total of 101 patients were identified (32 LGFMS, 50 SEF, 19 H-LGFMS/SEF). Median (m) follow-up was 62.1 months. mPFS-1 was 28.7, 11.8, and 20.3 months for LGFMS, SEF, and H-LGFMS/SEF, respectively. mOS was 145.8, 41.9, and 113.5 months, respectively. Treatments included anthracycline-based chemotherapy, gemcitabine-based chemotherapy (G), pazopanib, trabectedin, others. mPFS-2 was: 20.1, 5.5, and 3.5 months in H-LGFMS/SEF, SEF, and LGFMS, respectively, with anthracyclines; 19.5, 7.7, and 6.9 months in LGFMS, SEF, and H-LGFMS/SEF, respectively, with pazopanib; 12.0, 9.7, and 3.1 months in H-LGFMS/SEF, LGFMS, and SEF, respectively. Occasional responses occurred with ifosfamide/oral cyclophosphamide, and prolonged stable disease with immune checkpoint inhibitors. CONCLUSIONS: In this series, the largest available, metastatic LGFMS, SEF, and H-LGFMS/SEF showed different courses. Systemic agents have modest efficacy, informing future trials of novel agents for these tumours.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".