The Influence of Older Age on Physician and Patient Decision-Making Regarding Enrollment to Breast Cancer Clinical Trials.
Bibliographic record
Abstract
Abstract Background: More than 50% of breast cancers occur in women ≥65 years. Clear guidelines for treatment do not exist for this population, however, due to underrepresentation of older patients on clinical trials. We reported that patients 65 and older are significantly underrepresented in Southwest Oncology Group (SWOG) trials, particularly in breast cancer. (Hutchins, 1999) We conducted a prospective study (S0316) to determine physician- and patient-perceived barriers to breast cancer clinical trial enrollment in older versus younger patients.Methods: Eight geographically diverse SWOG institutions, 5 academic and 3 community, participated in S0316. Breast cancer patients were registered at time of systemic treatment decision-making. The study prospectively assessed reasons behind patients' and physicians' decisions to either enroll in or decline clinical treatment trials, including demographics, return rates to the institution, trial availability, and eligibility. Patient questionnaires elicited concerns about treatment toxicities, confidence in medical staff or institution, opposition or support by family/friends, and financial or time commitment concerns. Physician questionnaires elicited factors influencing decisions either not to discuss a trial or not to enroll the patient, including treatment toxicities, patient age or medical status, demands on personal or staff time, and reimbursement issues. Results were compared between patients <65 vs. ≥65 years.Results: 1,079 patients were registered and eligible, and 909 (84%) returned for follow-up. Clinical trial participation was 16%. The major reason for non-accrual was either trial unavailability or ineligibility (60%). Older patients were less likely to be eligible for trials (65% vs. 78%, p=.004). If eligible, trial participation rates did not differ significantly by age (34% vs. 40%, p=.32). Treatment-specific issues were the most common reasons cited by all patients for non-participation. Patients ≥65 more often were concerned about side effects (p=.02), had friends opposed to participation (p=.001), or believed that participation would not benefit other generations (p=.009). Concerns about transportation, time commitment, or posing a burden to family were similar between age groups. Physicians discussed trial participation when trials were available and patients were eligible with 76% <65 years versus 58% ≥65 years (p=.008). The study regimen and toxicity were the most common reasons influencing physician decisions not to discuss a trial, but did not differ between age groups. For patients ≥65 years, 14% of physicians indicated age as a reason the patient did not participate vs. 3% for patients <65 years (p=.002).Conclusions: Trial unavailability or patient ineligibility are major reasons for lack of enrollment on breast cancer clinical trials for patients of all ages in this prospective study. Older patients were less likely to be eligible for trials, but if eligible participated at similar rates to younger patients. Older age should not deter physicians in recommending clinical trials. Addressing stringent eligibility criteria may improve accrual rates of older patients.Supported by the Breast Cancer Research Foundation and the SWOG Hope Foundation Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3077.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".