Poor Accrual in Palliative Research Studies: An Update From the Rapid Response Radiotherapy Program
Bibliographic record
Abstract
BACKGROUND: In June 2003, the Rapid Response Radiotherapy Program (RRRP) implemented changes to recruitment strategies in attempts to increase patient accrual to research studies. Such modifications included the use of a dedicated research assistant to screen for and identify eligible study patients, the introduction of more appropriate inclusion criteria, and the switch towards telephone interviews to minimize patient burden. The purpose of this study is to provide an update on patient accrual in the RRRP. METHODS: All patients seen in the RRRP from January 2002 to December 2009 were recorded in a prospective database. Reasons for referral, eligibility for clinical trials, reasons for non-accrual, and various demographics information were recorded. Descriptive statistics summarized findings. RESULTS: A total of 4726 patient visits were recorded from January 1st, 2002 to December 31st, 2009. Prior to changes, the overall rate of accrual into research studies was 14.9% versus 48.1% after changes were implemented. Patients were not accrued onto studies mainly to due ineligibility according to study protocol. Other reasons such as language barrier (12.1%), physician objection (3.5%), patient declining participation (11.3%) and lack of a research assistant (9.3%) were cited. CONCLUSIONS: Changes in clinical structure and study design can significantly impact accrual patterns in palliative radiotherapy studies. Despite these changes however, the majority of patients are still not enrolled in studies. Therefore additional efforts need to be made to maximize patient accrual and minimize attrition.
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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 | 0.386 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier 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".