Can the Referring Surgeon Enhance Accrual of Breast Cancer Patients to Medical and Radiation Oncology Trials? The Enhance Study
Bibliographic record
Abstract
INTRODUCTION: The accrual rate to clinical trials in oncology remains low. In this exploratory pilot study, we prospectively assessed the role that engaging a referring surgeon plays in enhancing nonsurgical oncologic clinical trial accrual. METHODS: Newly diagnosed breast cancer patients were seen by a surgeon who actively introduced specific patient-and physician-centred strategies to increase clinical trial accrual. Patient-centred strategies included providing patients, before their oncology appointment, with information about specific clinical trials for which they might be eligible, as evaluated by the surgeon. The attitudes of the patients about clinical trials and the interventions used to improve accrual were assessed at the end of the study. The primary outcome was the clinical trial accrual rate during the study period. RESULTS: Overall clinical trial enrolment during the study period among the 34 participating patients was 15% (5 of 34), which is greater than the institution's historical average of 7%. All patients found the information delivered by the surgeon before the oncology appointment to be very helpful. Almost three quarters of the patients (73%) were informed about clinical trials by their oncologist. The top reasons for nonparticipation reported by the patients who did not participate in clinical trials included lack of interest (35%), failure of the oncologist to mention clinical trials (33%), and inconvenience (19%). CONCLUSIONS: Accrual of patients to clinical trials is a complex multistep process with multiple potential barriers. The findings of this exploratory pilot study demonstrate a potential role for the referring surgeon in enhancing nonsurgical clinical trial accrual.
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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 | 0.091 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".