Data harmonization for collaborative research among MS registries: A case study in employment
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility of collaboration and retrospective data harmonization among three multiple sclerosis (MS) registries by investigating employment status. METHODS: We used the Maelstrom guidelines to facilitate retrospective harmonization of data from three MS registries, including the NARCOMS (North American Research Committee on MS) Registry, German MS Register (GMSR), and United Kingdom MS (UK-MS) Register. A protocol was developed based on the guidelines, and summary-level data were used to combine results. Employment status and a limited set of factors associated with employment (age, sex, education, and disability level) were harmonized. A meta-analytic approach was used to pool estimates using a weighted average of logistic regression estimates and their variances in a random effects model. RESULTS: Employment status, age, sex, education, and disability were mapped. The overall employment rate was 57% (11,143 employed out of 19,562 persons with MS) with the GMSR having the highest proportion of participants employed (66.2%), followed by the UK-MS (55.2%) and NARCOMS (43.0%) registries. As disability level increased, the odds of not being employed increased. CONCLUSION: Harmonization across registries was feasible. The Maelstrom guidelines provide a valuable roadmap for conducting high-quality harmonization projects. The pooling of data sources has the potential to be an important mechanism for conducting research in MS.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".