Master protocol for a series of cohort-based randomized controlled trials to test tools to communicate research results to study participants and others with relevant lived experience: the SPIN-CLEAR Trials
Bibliographic record
Abstract
BACKGROUND: Research results are often not communicated to study participants or others with relevant lived experience. Effective communication of research results would help study participants understand their contribution to research and could improve trust in research and likelihood of research participation. Few randomized controlled trials (RCTs), however, have compared the effectiveness of research communication tools, and it is not known which tools work best for different people. We will conduct the Scleroderma Patient-centered Intervention Network-Communicating Latest Evidence and Results (SPIN-CLEAR) trial series via the multi-national SPIN Cohort to compare tool effectiveness. Primary objectives of each RCT will be to compare tools based on (1) information completeness, (2) understandability, and (3) ease of use. We will additionally evaluate comprehension of key aspects of disseminated research; likelihood that participants would enroll in a similar future study; and, for all primary and secondary outcomes, outcomes by participant characteristics (gender, age, race or ethnicity, country, language, education level, health literacy). METHODS: An advisory team of people with systemic sclerosis (SSc, also known as scleroderma) participated in developing research questions, selecting outcomes, and designing the series of parallel-arm RCTs that will each compare two or more tools or tool variations to a plain-language summary comparator; the common comparator will facilitate across-trial comparisons. In each RCT, people with SSc and researchers will select a recent SSc research study to disseminate. Tools will be developed by experienced tool developers and people with SSc. SPIN Cohort participants (current N eligible = 1522 from 50 SPIN sites in Australia, Canada, France, UK, USA) and additional participants recruited via social media and patient organization partners who consent to participate will be randomized to a dissemination tool or plain-language summary comparator and complete outcomes. Analyses will be intent-to-treat and use linear regression models. DISCUSSION: Each trial in the planned series of trials will build upon knowledge from previous trials. Results will contribute to the evidence base on how to best disseminate results to study participants and others with relevant lived experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT06373263. Registered on April 17, 2024 (first trial in series).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | MetaresearchScholarly communication Domain: Reporting · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | medium |
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 | 0.144 | 0.249 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.191 | 0.052 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".