Determinants of Accrual to Prostate Cancer Clinical Trials
Bibliographic record
Abstract
Purpose : Progress in the management of prostate cancer depends on the timely completion of clinical trials that address important relevant treatment questions. This study examines the accrual process for prostate cancer clinical trials to elicit specific reasons why patients are not being accrued at higher rates at two Canadian cancer centers. Methods : A chart review was conducted on all prostate cancer patients who were referred for a treatment consultation at the centers during two, one-month periods. Patients were prospectively tracked for trial eligibility, whether they were approached for trial participation, and if so whether they accepted or declined. Telephone interviews were conducted in one of the centers on those patients who were approached to determine the reasons for their decisions regarding trial participation. Results : Of 359 patients seen for a treatment decision, 48.2% were eligible for at least one trial, 67.6% of eligible patients were approached to participate in a trial, and 46.2% of those approached were accrued. This resulted in an overall accrual rate of 15.0%. There were significant differences in the interview responses of trial acceptors and decliners to some questions. Conclusion : Accrual may be increased not only by designing trials that address issues in patients with common clinical characteristics but also by broadening eligibility criteria, and by emphasizing the benefits of trial participation to potential patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.200 | 0.543 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".