Patient and Public Involvement in Inflammatory Bowel Disease Research—A Scoping Review
Bibliographic record
Abstract
Background: Interest in patient and public involvement in research has grown. Medical, health, and social care research has demonstrated several benefits of patient and public engagement, such as empowering user input and reducing attrition rates in clinical trials. To date, no study has reviewed patient engagement in inflammatory bowel disease (IBD). We aimed to describe the benefits, challenges, and best practices of patient engagement in IBD research. Methods: We performed a systematic search on MEDLINE, EMBASE, and Cochrane for all clinical IBD research studies in which patients were involved in the research process (1946- 2023). Patient input was considered in: (1) study design, (2) study execution, (3) research dissemination, and/or (4) other domains not specified here. Two authors independently screened and extracted data on type of engaged person(s), format of engagement, author-reported benefits, recommendations, and challenges. For each study, we reported the level of patient engagement and study adherence to standardized reporting guidelines. Results: After screening 9,355 articles, we included 51 for final analysis. IBD patients were most frequently engaged in study design. Patient engagement in IBD research improved recruitment rates and promoted the creation of user-friendly quality-of-life tools. Selection bias and recruitment difficulties were common challenges in the application of patient engagement. Authors recommended continuous patient involvement to address emerging priorities and cognitive interviewing to improve questionnaire clarity. Conclusions: Patient engagement represents an important step in promoting patient-centred care. According to study authors, implementing cognitive interviewing techniques, continuous patient involvement, and standardized reporting guidelines may improve future iterations of engagement in IBD.
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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 | 0.078 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".