Including patients in core outcome set development: issues to consider based on three workshops with around 100 international delegates
Bibliographic record
Abstract
Plain English summary This commentary article describes three interactive workshops that explored how patients can contribute to decisions about what outcomes are measured in clinical trials across the world. Outcomes like quality of life, side-effects and pain are used in trials to measure whether a treatment is effective. Here, we outline how research groups are increasingly coming together to develop ‘core outcomes sets’ for particular conditions. Core outcome sets are lists of agreed outcomes. Their use will help in identifying which treatments are effective by enabling people to compare the findings of different clinical trials in the same condition. Currently, it is often very difficult to make these comparisons because different studies often measure different outcomes. Delegates attending the workshops included patients, clinicians and researchers. They discussed ways of making core outcome set development more meaningful and accessible for patients, and ensuring that they have a genuine say in the development process. This article summarises these discussions and concludes by identifying three distinctive challenges in securing patient input to core outcome set development: the process and objectives can seem far removed from the immediate concerns of patients, difficulties can arise in securing patient input on an international scale, and difficulties can also arise in bringing multiple stakeholder groups together to achieve consensus. While patient participation, involvement and engagement in core outcome set development can draw on lessons from other research areas, these distinctive challenges point to the need for distinctive solutions to enable meaningful patient input to core outcome set development. Abstract Background This article describes three workshops that explored how patients can contribute to decisions about what outcomes are measured in clinical trials. People need evidence about what treatments are best for particular health conditions. The strongest evidence comes from systematic reviews comparing outcomes across different studies of treatments for a particular condition. However, it is often difficult to do these comparisons because the different studies—even though they have all investigated the same condition—often measure different outcomes. To tackle this problem, research teams are increasingly coming together to develop core outcome sets (COS) for particular conditions or treatments. The goal is that across the world, all the research teams working on the same condition or treatment will then use the COS in their research. Main body We report on three interactive workshops that explored how patients and the public can contribute to decision making about what outcomes should be included in a COS. About 100 international delegates, including researchers, clinicians and patients, attended the workshops. The workshops were held in the United Kingdom, Italy and Canada as part of the COMET (Core Outcome Measures in Effectiveness Trials) Initiative annual meetings. Patients who had some experience as research advisors, collaborators, partners or co-ordinators facilitated the workshops together with a researcher. Notes made during each workshop informed the preparation of this article. Workshop discussion focussed on ways of making core outcome set development more meaningful and accessible for patients. Delegates wanted patients to have a genuine say, alongside other stakeholders, in what outcomes are included in COS. Delegates felt that key to ensuring this is recognising that patient participation in COS development alone is not enough, and that patients will also need to be involved in the design of COS development studies. Conclusion We conclude by pointing to some distinctive challenges in including patients in COS development. While the COS development community can draw on the lessons learnt from other research areas about patient participation, involvement and engagement, the distinctive challenges that arise in COS development point to the need for some distinctive solutions too.
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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 | 0.146 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.016 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".