Identifying Barriers to Accrual in Radiation Oncology Randomized Trials
Bibliographic record
Abstract
Background: Data about factors driving accrual to radiation oncology trials are limited. In oncology, 30%–40% of trials are considered unsuccessful, many because of poor accrual. The goal of the present study was to inform the design of future trials by evaluating the effects of institutional, clinician, and patient factors on accrual rates to a randomized radiation oncology trial. Methods: Investigators participating in sabr-comet (NCT01446744), a randomized phase ii trial open in Canada, Europe, and Australia that is evaluating the role of stereotactic ablative radiotherapy (sabr) in oligometastatic disease, were invited to complete a survey about factors affecting accrual. Institutional ethics approval was obtained. The primary endpoint was the annual accrual rate per institution. Univariable and multivariable linear regression analyses were used to identify factors predictive of annual accrual rates. Results: On univariable linear regression analysis, off-trial availability of sabr (p = 0.014) and equipoise of the referring physician (p = 0.014) were found to be predictive of annual accrual rates. The annual accrual rates were lower when centres offered sabr for oligometastases off-trial (median: 3.7 patients vs. 8.4 patients enrolled) and when referring physicians felt that, compared with having equipoise, sabr was beneficial (median: 4.8 patients vs. 8.4 patients enrolled). Multivariable analysis identified perceived level of equipoise of the referring physician to be predictive of the annual accrual rate (p = 0.023). Conclusions: The level of equipoise of referring physicians might play a key role in accrual to radiation oncology randomized controlled trials. Efforts to communicate with and educate referring physicians might therefore be beneficial for improving trial accrual rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.564 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".