Pragmatic paradigm for patient-reported outcome measure selection in lymphoma clinical trials: a rapid review study
Bibliographic record
Abstract
OBJECTIVES: Lymphoma is a haematologic malignancy affecting cells of the immune system. With numerous treatment options available, clinicians and patients frequently face difficulty in selecting the most appropriate therapy. Patient-reported Outcome (PRO) offers valuable patients' insight that may support treatment differentiation. A PRO measure (PROM) is a questionnaire or survey measuring a PRO. Despite many efforts to guide the selection of PROMs in clinical trials, choosing the appropriate ones remains a challenge. This study aims to develop a pragmatic paradigm for selecting PROMs in clinical trials involving adult patients with lymphoma through the collaboration and communication between clinical investigators involved in trials and Health Economics and Outcomes Research (HEOR) scientists specialised in research methodologies. DESIGN: A rapid review was conducted to identify existing PROMs for adult patients with lymphoma in clinical trials and guidelines supporting PROM selection. DATA SOURCES: PubMed, Google Scholar and websites for regulatory and health technology assessment (HTA) bodies in eight countries of interest were searched from 2009 to July 2024. ELIGIBILITY CRITERIA: Publications with PROMs were identified for adult patients in lymphoma trials. The most relevant guidelines supporting the development of the pragmatic paradigm were selected. DATA EXTRACTION AND SYNTHESIS: The initial search and data extraction were conducted by one author. All authors participated in an in-depth review process. RESULTS: We categorise 31 applicable PROMs for lymphoma into four distinct groups, streamlining the PROM selection process to facilitate effective communication among clinical investigators, HEOR scientists, patients and others. Additionally, a five-step pragmatic paradigm is developed for identifying appropriate PROM(s). CONCLUSIONS: The pragmatic paradigm presents a practical approach for selecting PROM(s) in lymphoma clinical trials. An appropriate PROM should conceptually align with the treatment goals and be acceptable to regulatory and HTA bodies. Thus, lymphoma clinical trials can generate more patient-focused data, contributing to improving patients' quality of life and advancing lymphoma 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 | 0.355 | 0.639 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".