Equivalent Disease-Specific Survival Between Rural and Urban Osteosarcoma Patients: A Retrospective Analysis of the SEER Database
Bibliographic record
Abstract
Osteosarcoma is the most common primary malignancy of bone. Previous studies have demonstrated rural-urban disparities in metastatic disease incidence and overall survival in high-grade osteosarcoma patients. However, there is a paucity of literature investigating disease-specific survival (DSS) disparities between rural and urban patients, which is explored herein using the SEER database. Patients with biopsy-proven cases of osteosarcoma were identified from 2000–2021. Statistical analysis was completed using SPSS version 29.0.2 and included chi-squared, Kaplan–Meier and log-rank, and stepwise Cox regressions. Statistical significance was considered at p < 0.05. Kaplan–Meier analysis revealed no significant differences in 5- and 10-year DSS between rural (55.0% and 47.0%) and urban patients (56.0% and 51.0%) (p = 0.107). Multivariable analysis further revealed no significant DSS difference between rural and urban patients (aHR: 1.03; 95% CI: 0.86–1.24; p = 0.757). This study expands upon prior research by investigating DSS between rural and urban osteosarcoma patients and finding no significant differences. While rural living is often associated with worse outcomes, important prognostic factors for osteosarcoma, including metastatic disease at presentation and tumor grade, were not significantly different between rural and urban patients in our study, possibly explaining our DSS-related findings. Factors other than geographical location likely impact outcomes, and future research should examine other ways that rural living may influence cancer care.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".