Abstract P5-13-01: Does empowering patients improve accrual to breast cancer trials?
Bibliographic record
Abstract
Abstract BACKGROUND Many international oncology professional and patient advocacy groups recommend that a minimum of 5% of eligible new patients should be entered into a clinical trial. Unfortunately, physician and patient related barriers translates to a much lower accrual rate in reality. We performed a prospective single arm pilot study evaluating the efficacy of implementing various physician and patient related strategies in enhancing clinical trial accrual. METHODS All patients with newly diagnosed breast cancer seen at the breast surgery clinic were eligible for the study. Patients were offered information packages by their surgeons on non-surgical clinical trials that they might be eligible for, prior to their initial oncologist visit. Oncologists were informed of the information given to the prospective patient consultation via email and chart flagging. Patient were then given a questionnaire assessing the feedback on this method of introducing clinical trials. The primary outcome was the number of patients consenting to clinical trials. Secondary outcomes included the number of patients actually enrolling in clinical trials, screen failure rate, and overall patient satisfaction with this method of potentially enhancing accrual to clinical trials. RESULTS 36 patients consented to this pilot study. 51% of oncologists mentioned the clinical trials to the patients. For those patients with which clinical trials were discussed, 72% went on to consent for a trial of which 31% were ultimately found to be ineligible (screen failure rate). The overall 14% clinical trial enrolment was significantly higher than our historical average of 7%. 100% of the patients found that the information package very helpful and was noted to reduce anxiety (39%) and empower (31%) the patients. 19% of the patients felt that this information should have been offered by the oncologist during the initial consultation as opposed to the surgeon prior to the oncology visit. CONCLUSIONS The findings of this study could have a major impact on the way that cancer centres across the world approach patients for clinical trial options. Physicians remain an important barrier to trial accrual. The results of this study demonstrate that combined patient and physician centered approach to clinical trial enrolment may be the most effective. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P5-13-01.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".