Deciding individual treatment for primary retroperitoneal sarcoma
Bibliographic record
Abstract
INTRODUCTION: Retroperitoneal sarcomas (RPS) are rare mesenchymal tumors that account for only 0.1-0.2% of all malignancies. Management of this disease is challenging, and resection remains the cornerstone of treatment. Ongoing international collaboration has expanded our knowledge of this disease, allowing for a more personalized approach to RPS patients resulting in improved survival over time. Due to the heterogeneity of RPS, with differing recurrence patterns and sensitivities to neoadjuvant therapies based on histology and grade, management of RPS should be tailored to the individual patient. AREAS COVERED: Our review focuses on a histology-driven approach in the management of primary RPS. We searched relevant articles from 1993 to 2023 that investigated prognostic factors and treatment of patients with RPS and summarized recent advances and future directions in the field. EXPERT OPINION: Deeper understanding of the role of neoadjuvant radiotherapy and ongoing trials investigating the role of neoadjuvant chemotherapy will potentially contribute to the development of individualized treatment pathways.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".