Including the patient voice in the development and implementation of patient‐reported outcomes in cancer clinical trials
Bibliographic record
Abstract
CONTEXT: Patient-reported outcomes (PROs) are used in parallel with clinical evidence to inform decisions made by industry, clinicians, regulators, health technology assessment bodies and other health-care decision-makers. In addition, PRO data can also guide shared decision making and individual patient choice. Yet, the quality of many PROs in cancer clinical trials is suboptimal and requires improvement to add value to health care and policy decision making. OBJECTIVE: To show how the integration of the patient and/or patient advocate at all stages of PRO development can help to realize the full potential of PROs. METHODS: We examined the literature to show that the patient voice is often absent from the planning and implementation of PROs in cancer clinical trials. Good practice examples from the literature were combined with guideline recommendations, training or educational resources, and our own experience to create detailed practical steps for the inclusion of patients and/or patient advocates throughout PRO development. RESULTS: Patient or patient advocates can play an active role in shaping PROs that are meaningful to the patient. They can contribute to content, choice of medium and implementation in a way that may support PRO completion and minimize missing data. Patients and their advocates can work to ensure PRO findings are disseminated appropriately in a way that is accessible to patients. CONCLUSION: This practical guidance aims to optimize PRO development and implementation in clinical trials, resulting in robust, relevant data that reflect the patient experience and that support decisions made by all stakeholders involved in research and health care.
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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 | 0.715 | 0.739 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".