Global Variations in Patterns of Care for Retroperitoneal Sarcoma
Bibliographic record
Abstract
OBJECTIVE: To examine variations in patterns of care for retroperitoneal sarcoma (RPS) among sarcoma centres globally, including diagnostic work-up, surgical strategies and (neo)adjuvant therapies. METHODS: Retrospective analysis for primary RPS, from 19 RPS referral centres worldwide, prospectively collected within the RESAR repository (NCT03838718) between Feb 2017 - July 2022. Centres were categorised high volume (HVC) or low volume (LVC). Comprehensive resection (CR) was defined as en-bloc resection of ipsilateral kidney and colon. RESULTS: 1718 primary RPS were included. Preoperative biopsy was utilised frequently (median rate 98%) for solid (non-liposarcoma) RPS. In liposarcoma, the median rate of CR was 64%, with wide variation (IQR 37% [43%-80%], range 0-100%). There was greater variation in CR in liposarcoma in LVC (IQR 39.5% [40.5%-80%]) versus HVC (IQR 9.5% [58.3%-67.8%]). Perioperative chemotherapy was seldom used for liposarcoma (median 0%), with higher rates for leiomyosarcoma (median 10%) with high variation (IQR 26% [2%-28%]). Radiotherapy was used consistently infrequently in leiomyosarcoma (IQR 13% [0%-13%]. There was higher use of radiotherapy in HVC than LVC (median HVC 18.5% vs. LVC 5%). There was a significant decrease in radiotherapy use after the STRASS trial (pre 19% vs. post 14%, P=0.045). CONCLUSIONS: Low variation was found in pre-operative biopsy of non-liposarcomas, use of chemotherapy in liposarcoma and radiotherapy in leiomyosarcoma, suggesting agreement between centres. There was high variation, suggesting equipoise, in the role of chemotherapy in leiomyosarcoma and the value of CR in liposarcoma. The STRASS study results seem to have been accepted, with a reduction in radiotherapy after its publication.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".