Harmonization: a methodology for advancing research in multiple sclerosis
Bibliographic record
Abstract
Decreasing research funding is in conflict with the increasing need to conduct large studies to examine rare risk factors and interactions between risk factors. As a result, investigators are searching for strategies to stretch research funds and to design studies that will maximize investments already made. Multiple sclerosis (MS) is generally accepted as a multifactorial disease, and the assessment of interactions between risk factors and the desire to assess risk factors within particular sub-groups requires a large number of participants. Harmonization is a methodology that may help address this problem. Harmonization is a methodological approach that aims to systematize the process of combining individual data that are collected in several observational studies. Combining data will increase sample size, but the quality of the harmonized result is only as high as the quality of the individual studies and the comparability of the constructs measured. In this short report, we introduce the concept of harmonization and provide examples where harmonization may be advantageous in MS research.
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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 | 0.515 | 0.645 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.009 | 0.029 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".