A Prospective Analysis of the Influence of Older Age on Physician and Patient Decision-Making When Considering Enrollment in Breast Cancer Clinical Trials (SWOG S0316)
Bibliographic record
Abstract
Abstract Learning Objectives After completing this course, the reader will be able to: Cite reasons given by patients older than 65 years for their decisions to participate or not to participate in clinical trials.Cite reasons given by physicians for their decisions not to enroll patients older than 65 years in clinical trials or discuss enrollment with these patients. CME This article is available for continuing medical education credit at CME.TheOncologist.com Purpose. Patients older than 65 years are underrepresented in clinical trials. We conducted a prospective study (SWOG S0316) to determine physician- and patient-perceived barriers to breast cancer clinical trial enrollment for older patients. Methods. Eight geographically diverse SWOG institutions participated. The study assessed patients' and physicians' decisions to enroll in or decline clinical treatment trials, including demographics, trial availability, and eligibility. Patient and physician questionnaires elicited concerns related to treatment, medical status, age, family, and financial or transportation concerns. Results. A total of 1,079 patients were registered and eligible and 909 (84%) returned for follow-up. The major reason for nonaccrual was either trial unavailability or ineligibility (60%). Older patients were less likely to be eligible for trials (65% for age ≥65 years vs. 78% for age <65 years). If eligible, trial participation rates did not differ significantly by age (34% for age ≥65 years vs. 40% for age <65 years). Patients ≥65 years more often were concerned about side effects, had friends opposed to participation, or believed that participation would not benefit other generations. When trials were available and patients were eligible, physicians discussed trial participation with 76% of patients <65 years versus 58% of patients ≥65 years of age. For patients ≥65 years, 11% of physicians indicated age as a reason they did not enroll a patient in a clinical trial. Conclusion. Trial unavailability or patient ineligibility were the major reasons for lack of enrollment in 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 they participated at similar rates to younger patients.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".