Survival Outcomes of Ewing Sarcoma and Rhabdomyosarcoma by High- versus Low-Volume Cancer Centres in British Columbia, Canada
Bibliographic record
Abstract
Due to the rarity and complexity of treatment for Ewing sarcoma and rhabdomyosarcoma, studies demonstrate improved patient outcomes when managed by a multidisciplinary team at high-volume centres (HVCs). Our study explores the difference in outcomes of Ewing sarcoma and rhabdomyosarcoma patients based on the centre of initial consultation in British Columbia, Canada. This retrospective study assessed adults diagnosed with Ewing sarcoma and rhabdomyosarcoma between 1 January 2000 and 31 December 2020 undergoing curative intent therapy in one of five cancer centres across the province. Seventy-seven patients were included, 46 seen at HVCs and 31 at low-volume centres (LVCs). Patients at HVCs were younger (32.1 vs. 40.8 years, p = 0.020) and more likely to receive curative intent radiation (88% vs. 67%, p = 0.047). The time from diagnosis to first chemotherapy was 24 days shorter at HVCs (26 vs. 50 days, p = 0.120). There was no significant difference in overall survival by treatment centre (HR 0.850, 95% CI 0.448–1.614). Variations in care exist amongst patients treated at HVCs vs. LVCs, which may reflect differences in access to resources, clinical specialists, and varying practice patterns across centres. This study can be used to inform decisions regarding triaging and centralization of Ewing sarcoma and rhabdomyosarcoma patient treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".