Systematic Review of Barriers to the Recruitment of Older Patients With Cancer Onto Clinical Trials
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.
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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 | 0.075 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".