Barriers to patient accrual in Ontario oncology clinical trials: Attitudes of oncologists and clinical research personnel.
Bibliographic record
Abstract
36 Background: The medical specialty of oncology relies heavily on clinical trials to advance policies and practices related to cancer care. However, oncology clinical trial accrual in Ontario has dropped from 12.4% in 2007, to 8.5% in 2009. The objective of this study was to determine barriers experienced by Oncologists and Clinical Research Personnel (CRP) in recruiting patients to oncology trials in Ontario. Methods: In June 2012, an electronic survey was emailed to about 400 oncologists and CRP across Ontario. Variables of interest included demographic data, clinical trial involvement, and perceived barriers to participation in clinical trials amongst three previously identified barrier domains. Barriers were ranked, from 1 (least significant) to 5 (most significant). Statistics were compiled using Graphpad Prism software. Differences in responses were analyzed using the Kruskal – Wallis test and Dunn’s Multiple Comparison Test. Results: Of the 400 emails sent, there were 126 respondents (32%). Of the 126 respondents, 82 fully completed the survey (64.6% useable response rate). Amongst system related barriers, “time related” (Median Agreement (M): 4, Inter Quartile Range (IQR): 3-5), and “resource related” barriers (M: 4, IQR: 3-5) had the most negative effect on accrual (p<0.05). Amongst trial design barriers, “Relevance to patient population” (M: 3, IQR: 3-5), “Deviation from Standard of Care” (M: 3, IQR: 3-5) and “Complexity of Trial Protocol” (M: 4, IQR: 3-5) were the most significant barriers (p<0.05). Lastly, amongst personal barriers, “Commitment of the Principal Investigator/Research Staff” (M: 4, IQR: 3-5) and Drug Safety (M: 4, IQR: 2-4) were the most significant barriers to recruitment (p<0.05). Conclusions: Multiple barriers were identified as having a significant impact on patient accrual in clinical trials. Addressing these barriers prospectively in clinical trial design may benefit future studies to successfully accrue cancer patients. Also, creating clinical trial collaboration vehicles amongst sites in similar geographical areas may contribute to improving patient accrual to clinical trials.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".