Dementia with Lewy bodies research consortia: A global perspective from the ISTAART Lewy Body Dementias Professional Interest Area working group
Bibliographic record
Abstract
Dementia with Lewy bodies (DLB) research has seen a significant growth in international collaboration over the last three decades. However, researchers face a challenge in identifying large and diverse samples capable of powering longitudinal studies and clinical trials. The DLB research community has begun to focus efforts on supporting the development and harmonization of consortia, while also continuing to forge networks within which data and findings can be shared. This article describes the current state of DLB research collaborations on each continent. We discuss several established DLB cohorts, many of whom have adopted a common framework, and identify emerging collaborative initiatives that hold the potential to expand DLB networks and diversify research cohorts. Our findings identify geographical areas into which the global DLB networks should seek to expand, and we propose strategies, such as the creation of data-sharing platforms and the harmonization of protocols, which may further potentiate international collaboration.
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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 | 0.406 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".