Perspectives of People With Multiple Sclerosis Regarding Data Linkage and Sharing
Bibliographic record
Abstract
OBJECTIVES: Linkage of clinical trial data to other data such as administrative data could enhance understanding of long-term outcomes. We investigated attitudes of people with multiple sclerosis (MS) regarding external linkage of clinical trial data. METHODS: In a cross-sectional survey, North American Research Committee on Multiple Sclerosis registry participants reported willingness to share identifiers to support linkage of their clinical trial data to administrative health databases and preferences for long-term trial follow-up. Polytomous regression tested factors associated with agreeing to administrative data linkage. RESULTS: Of 6,998 potential participants, 4,980 (71.2%) responded. Of 4,662 respondents meeting eligibility criteria, 3,524 participants (75.6%) indicated that they would agree or might agree to allow administrative data access. Participants were most willing to share their initials (43.7% definitely, 26.8% perhaps). Higher education (odds ratio [OR] 1.51; 1.19-1.93) and income (≥$100,000 vs <$50,000 OR 1.74; 1.17-2.59), alcohol consumption (OR range 1.77-2.27), and previous trial participation (yes/no, OR 1.89; 1.44-2.49) were associated with willingness to allow data access while Black race was associated with unwillingness (0.41; 0.20-0.82). DISCUSSION: A substantial proportion of people with MS would potentially agree to data sharing and linkage to support clinical trials. Future studies should establish generalizability of these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".