Accrual of Older Patients With Breast Cancer to Alliance Systemic Therapy Trials Over Time: Protocol A151527
Bibliographic record
Abstract
Purpose Despite increasing awareness of accrual challenges, it is unknown if accrual of older patients to breast cancer treatment trials is improving. Methods We examined accrual of older patients to Alliance for Clinical Trials in Oncology systemic therapy breast cancer trials during 1985-2012 and compared disease characteristics and reasons for therapy cessation for older (age ≥ 65 years and ≥ 70 years) versus younger (age < 65 years and < 70 years) participants. To examine accrual trends, we modeled age as a function of time, using logistic regression. Results Overall, 17% of study participants were ≥ 65 years of age. Approximately 15%, 24%, and 24% of participants in adjuvant, neoadjuvant, and metastatic trials were age ≥ 65 years, and 7%, 15%, and 13% were age ≥ 70 years, respectively. The odds of a patient age ≥ 65 years enrolling significantly increased over time for adjuvant trials (odds ratio [OR] per year, 1.04; 95% CI, 1.04 to 1.05) but decreased significantly for neoadjuvant and metastatic trials (OR, 0.62; 95% CI, 0.58 to 0.67 and OR, 0.98, 95% CI, 0.97 to 1.00). Similar trends were seen for those age ≥ 70 years but these were statistically significant for adjuvant and neoadjuvant trials only (OR, 1.05, 95% CI, 1.04 to 1.07; and OR, 0.57, 95% CI, 0.52 to 0.62). In general, those age ≥ 65 years ( v those < 65 years) in adjuvant studies had a higher mean number of lymph nodes involved and more hormone receptor-negative tumors, although tumor sizes were similar. Early protocol treatment cessation was also more frequent in those age ≥ 65 years (50%) versus < 65 years (35.9%) across trials. Conclusion Older patients with breast cancer remain largely underrepresented in cooperative group therapeutic trials. We observed some improvement in accrual to adjuvant trials but worsening of accrual for neoadjuvant/metastatic trials. Novel strategies to increase accrual of older patients are critical to meaningfully change the evidence base for this growing patient population.
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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 | 0.134 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.016 |
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".