Enrollment of Older Patients in Cancer Treatment Trials in Canada: Why is Age a Barrier?
Bibliographic record
Abstract
PURPOSE: To evaluate the enrollment of older patients (>/= 65 years) in Canadian cancer treatment trials and compare accrual of older patients in Canada and the United States. PATIENTS AND METHODS: A retrospective analysis of the number of older patients enrolled in National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) treatment trials between 1993 and 1996 was performed. These rates were compared with the corresponding rates in the general population of patients who were >/= 65 years old and had cancer, obtained from Statistics Canada, and those published by the Southwest Oncology Group (SWOG) in the United States. RESULTS: Between 1993 and 1996, 4,174 patients were enrolled onto 69 NCIC CTG trials of 16 tumor types. Older patients accounted for 22% of trial enrollees, compared with 58% of the Canadian population with cancer. This discrepancy existed in all cancer types except for multiple myeloma. The percentages of older patients enrolled were also analyzed by study type: 15% in adjuvant trials, 25% in metastatic trials, 29% in investigational new drug trials, 24% in phase I trials, and 21% in supportive care trials. The overall proportion of older patients enrolled onto Canadian trials (22%) was slightly lower than that in SWOG trials (25%). CONCLUSION: Age remains a barrier for accrual onto cancer treatment trials, even when reimbursement is not an issue. Strategies to overcome this barrier, including the implementation of trials specifically tailored to patients aged >/= 65 years, are prudent in light of our aging 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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".