Systematic Review of Barriers to the Recruitment of Older Patients With Cancer Onto Clinical Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Older patients are significantly underrepresented in cancer clinical trials. A literature review was undertaken to identify the barriers that impede the accrual of this vulnerable population onto clinical trials and to determine what specific strategies are needed to improve the representation of older patients in research studies. METHODS: A systematic literature search was undertaken using several different strategies to identify relevant articles. RESULTS: Nine of 31 relevant papers from 159 citations were included. Age is a significant barrier to recruitment; only a quarter to one third of potentially eligible older patients are enrolled onto trials. Physicians' perceptions, protocol eligibility criteria with restrictions on comorbid conditions, and functional status to optimize treatment tolerability are the most important reasons resulting in the exclusion of older patients. Other barriers include the lack of social support and the need for extra time and resources to enroll these patients. Conversely, older patients do not view their age as an important reason for refusing trials. CONCLUSION: Specific clinical trials confined to older patients should be conducted to evaluate tumor biology, treatment tolerability, and the effect of comorbid conditions. Protocol designs need to stratify for age and be less restrictive with respect to exclusions on functional status, comorbidity, and previous cancers, such that results are generalizable to older patients. Physician education to dispel unfounded perceptions, improved access to available clinical trials, and provision of personnel and resources to accommodate the unique requirements of an older population are possible solutions to remove the barriers of ageism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.030 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it