A common outcome set for trials in dementia with Lewy bodies (DLB COS)
Bibliographic record
Abstract
INTRODUCTION: Methodological heterogeneity in dementia with Lewy bodies (DLB) trials contributes to publication bias and makes evidence synthesis and meta-analysis challenging. We aimed to develop a core outcome set for DLB (DLB COS) trials to improve consistency and comparability in DLB research. METHODS: We conducted a systematic review to identify outcomes and administered a two-stage Delphi survey to a diverse panel of lay and professional stakeholders. We asked respondents which outcomes should be prioritized and included in DLB COS. RESULTS: Forty-nine outcomes were presented to survey respondents. Consensus was reached regarding eight outcomes for the final DLB COS: delusions/paranoia; fluctuations in cognition, attention, and arousal; functioning; global cognition; hallucinations; quality of life; motor parkinsonism; and rapid eye movement sleep behavior disorder. DISCUSSION: If adopted, DLB COS can enhance the comparability of research findings and facilitate standardization and harmonization. Highlights: A systematic review revealed heterogeneity in dementia with Lewy bodies (DLB) study outcomes.Our study produced a DLB Core Outcome Set (DLB COS) comprising eight outcomes.DLB COS sets the minimum reporting standards for future trials.DLB-specific rating scales incorporating these outcomes are needed.Addressing this gap is a strategic priority in DLB 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 | 0.413 | 0.612 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".