DISP-30. Impact of distance traveled for clinical trial participation on clinical outcomes in patients with glioblastoma
Bibliographic record
Abstract
Abstract BACKGROUND Patients with glioblastoma (GBM) often face limited access to clinical trials, particularly those living in rural or underserved regions. We investigated whether travel distance for trial participation is associated with survival among these patients. METHODS We conducted a retrospective cohort study of adults with recurrent or progressive high-grade glioma enrolled in clinical trials at Dana-Farber Cancer Institute between 1999 and 2018. Travel distance from residence to Dana-Farber was estimated using ZIP codes, yielding the median distance per visit. Overall survival (OS) from trial enrollment was assessed using Kaplan-Meier analysis. Log-rank tests were used to evaluate survival differences, and Cox regression models were applied to examine the independent association between travel distance and OS, adjusting for age, income, education, and Karnofsky Performance Status (KPS). RESULTS At the time of submission, 92 patients were included, most of whom were white (98%) with a median KPS of 90 at enrollment. Median travel distance per visit was 85 miles. Travel distance was not significantly associated with OS in univariable (p = 0.14) or multivariable models (HR 1.0004, p = 0.12). In the univariable analysis, older age at progression (p = 0.00484), lower KPS at enrollment (p = 0.04641), and at the end of trial participation (p < 0.0001) were associated with worse OS. In multivariable analysis, distance per visit was not associated with OS (HR 1.0004, p = 0.12). Lower KPS at end of trial participation (HR 0.96, p < 0.0001) remained a strong predictor of worse OS. Education (HR 0.94, p = 0.058) showed borderline significance. CONCLUSION In this interim analysis, travel distance for clinical trial participation was not associated with survival in GBM. Additional data from recent years are being incorporated to strengthen our analysis.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".