Assessing patient-reported outcomes (PROs) and patient-related outcomes in randomized cancer clinical trials for older adults: Results of DATECAN-ELDERLY initiative
Bibliographic record
Abstract
As older adults with cancer are underrepresented in randomized clinical trials (RCT), there is limited evidence on which to rely for treatment decisions for this population. Commonly used RCT endpoints for the assessment of treatment efficacy are more often tumor-centered (e.g., progression-free survival). These endpoints may not be as relevant for the older patients who present more often with comorbidities, non-cancer-related deaths, and treatment toxicity. Moreover, their expectation and preferences are likely to differ from younger adults. The DATECAN-ELDERLY initiative combines a broad expertise, in geriatric oncology and clinical research, with interest in cancer RCT that include older patients with cancer. In order to guide researchers and clinicians coordinating cancer RCT involving older patients with cancer, the experts reviewed the literature on relevant domains to assess using patient-reported outcomes (PRO) and patient-related outcomes, as well as available tools related to these domains. Domains considered relevant by the panel of experts when assessing treatment efficacy in RCT for older patients with cancer included functional autonomy, cognition, depression and nutrition. These were based on published guidelines from international societies and from regulatory authorities as well as minimum datasets recommended to collect in RCT including older adults with cancer. In addition, health-related quality of life, patients' symptoms, and satisfaction were also considered by the panel. With regards to tools for the assessment of these domains, we highlighted that each tool has its own strengths and limitations, and very few had been validated in older adults with cancer. Further studies are thus needed to validate these tools in this specific population and define the minimum clinically important difference to use when developing RCTs in this population. The selection of the most relevant tool should thus be guided by the RCT research question, together with the specific properties of the tool.
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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 | 0.540 | 0.641 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".