Age barrier in clinical trials: Are we truly moving forward?
Bibliographic record
Abstract
208 Background: Older patients are underrepresented in clinical trials. This may in part be explained by the fact that, historically, there has often been an upper age limit for inclusion in studies. This is despite data that suggests that older patients are as likely as their younger counterparts to enroll in clinical trials if offered. Methods: All prospective, interventional phase III studies in GI malignancies registered with clinicaltrials.gov were identified. Currently opened and recruiting studies were included. Results: A total of 365 eligible trials were identified. One hundred and fifty (41%) were colorectal, 67 (18%) liver and 62 (16%) were pancreatic malignancies. Other malignancies were gastroesophageal, hepatobiliary and neuroendocrine tumours. A total of 172 (47%) studies had an upper age limit for inclusion. The most common tumor sites to have older age as an exclusion factor were - hepatobiliary (n = 10; 66%), liver (n = 39; 58%) and gastroesophageal (n = 31; 51%). The median age of exclusion was 75 years (range: 60-95). Chinese studies, which represented 30% (n = 111) of the total studies included, had the greatest proportion with an upper age limit (85%). The United States had the least, with only 3% of studies having an exclusion age. Conclusions: Significant progress had been made in recent years with many clinical trial protocols not having an upper age limit for inclusion. Study design and restrictive eligibity criteria need to be assessed and modified to ensure older patients are well represented in future clinical trials.
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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 | 0.667 | 0.759 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".