Travel distance to tertiary sarcoma centres does not influence oncological presentation or outcomes
Bibliographic record
Abstract
Aims: Soft-tissue sarcomas (STSs) are rare cancers with centralized care advocated to consolidate resources and expertise. However, geographical challenges, particularly in countries like Canada, can increase travel distances for patients. The impact of travel distance on sarcoma presentation and outcomes remains unclear, particularly in single-payer healthcare systems with centralized care. Methods: A retrospective cohort analysis was conducted on 1,570 patients with STS who underwent surgical resection at a Canadian tertiary referral centre between January 2010 and January 2021. Patients were divided into those living ≤ 50 km and > 50 km from the centre. Demographics, tumour characteristics, treatment methods, and survival outcomes were analyzed. A Cox regression model was constructed to evaluate predictors of overall survival. Results: Patients living > 50 km from the centre (n = 700) travelled a mean of 176 km (SD 250), while those ≤ 50 km (n = 870) travelled a mean of 24.8 km (SD 13.8). There were no significant differences in disease presentation, time to definitive treatment, use of systemic therapies, or functional outcomes between the two groups. The two-year and five-year overall survival rates were similar between the groups (83.1% (95% CI 80.1% to 86.1%) vs 83.8% (95% CI 81.8% to 85.8%) and 72.1% (95% CI 69.1% to 75.1%) vs 72.5% (95% CI 69.5% to 75.5%), respectively). The regression model demonstrated that age, higher tumour grade, depth, and lower income were predictive of worse overall survival, while distance travelled was not an independent predictor of survival. Conclusion: Contrary to previous studies, our findings suggest that travel distance did not influence disease presentation or survival outcomes in STS patients treated at a centralized sarcoma centre. This challenges previous notions regarding the impact of travel distance on cancer outcomes, and supports the effectiveness of centralized care models, even in geographically vast regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".