Bridging research gaps in geriatric oncology: unraveling the potential of pragmatic clinical trials
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review examines the role of pragmatic clinical trials (PCTs) in addressing the underrepresentation of older adults with cancer (OAC) in clinical trials. Focusing on real-world evidence (RWE), it aims to provide a comprehensive overview of PCT utilization, emphasizing their potential to enhance treatment decisions and patient outcomes. Existing knowledge gaps in PCT implementation are also discussed. RECENT FINDINGS: PCTs are identified as effective tools to include OACs with comorbidities and complex conditions in research, bridging the representation gap. Despite their proven value in healthcare provision, their application in OAC contexts remains limited, hindering comprehensive understanding and inclusivity in clinical trials. SUMMARY: While randomized controlled trials (RCTs) are considered the gold standard in oncology research, OACs have historically been excluded, perpetuating underrepresentation. Furthermore, even in current oncology clinical development trials, this demographic continues to be underrepresented. PCTs offer a valuable avenue for the identification and evaluation of therapies within authentic RW contexts, encompassing various healthcare settings, such as hospitals, clinics, and physician practices. RCTs and PCTs complement one another, and the utilization of PCTs has the potential to inform clinical decision-making across the OACs entire treatment trajectory.
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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 | 0.204 | 0.477 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".