Engagement Methods in Brain Tumor Genomic Research: Multimethod Comparative Study
Bibliographic record
Abstract
BACKGROUND: Engaging patients, care partners, and others in research planning and conduct is increasingly valued. However, identifying the most effective ways to do so remains a challenge. OBJECTIVE: This study aimed to evaluate participation and participant experience using 3 engagement methods with the Low-Grade Glioma (LGG) Registry's Optimizing Engagement in Discovery of Molecular Evolution of Low-Grade Glioma (OPTIMUM) project, part of the National Cancer Institute's Participant Engagement and Cancer Genome Sequencing Network. METHODS: We evaluated LGG Registry research advisory council (RAC) meetings, Twitter (now known as X), and Facebook discussions across 4 engagement activities with each group. Researchers recorded discussions and performed qualitative content analysis to evaluate differences in the nature of interactions and recommendations for promoting trust and participation in LGG Registry research. Participants completed experience surveys after engagements 1 and 4 (Public and Patient Engagement Evaluation Tool, Research Engagement Survey Tool, Trust in Medical Researchers Scale, and Patient Engagement in Research Scale). RESULTS: RAC engagements involved 25 unique participants representing diverse backgrounds; tweet chats and Facebook discussions had 197 and 133 participants, respectively. Qualitative findings highlighted differences in the nature of interactions (eg, communication styles and types of information shared) across groups, but there was general agreement around recommendations for promoting participation in genomic research. Postengagement surveys (n=52 in ipostengagement activity 1; n=40 in postengagement activity 4) showed patterns suggesting a more positive experience overall for the RAC. CONCLUSIONS: Advisory councils and social media engagement methods have advantages and disadvantages. Advisory councils provide consistent interactions with the same individuals and clear procedures. Despite theoretically broader reach, social media engagement may yield less diverse perspectives. The LGG Registry aims to use RAC and social media engagement methods to promote diverse perspectives and maintain consistent interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.152 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".