Sociodemographic and clinical heterogeneity among longitudinal studies in dementia with Lewy bodies
Bibliographic record
Abstract
Abstract Background International collaboration is crucial to the future of research in dementia with Lewy bodies (DLB). Although it is hoped that a single global DLB cohort can be created through combination of data from the many longitudinal studies conducted internationally, a high likelihood of phenotypic heterogeneity may prohibit harmonisation and analysis of datasets. Our primary objective is to determine whether significant heterogeneity is observed in the sociodemographic and clinical characteristics of people with DLB globally. Method Longitudinal DLB studies and multi‐centre consortia were invited through existing professional networks to contribute limited descriptive clinical and sociodemographic data for their samples using a standardised proforma. One way analysis of variance (ANOVA) with summary statistics as input was used to compare mean scores. χ2 test was used to compare categorical variables. Results Descriptive data from 10 projects in seven countries, and one multinational consortium, were shared, comprising a pooled sample of 4157 individuals with DLB. The proportion of males (χ2 = 135, df = 7, p< .01) and the duration of formal education (F(3,6) = 19.41, p<.02) varied significantly between DLB projects. No significant difference in mean age (F(3,7) = 0.87, p = .61) or duration of DLB symptoms at baseline (F(2,6) = 1.46, p = 0.46) was noted, nor was it observed in scores of baseline Mini‐Mental State Examination (MMSE)(F(2,5) = 0.73, p = 0.66), Clinical Dementia Rating (CDR) (F(1,4) = 2.04, p = .48), or Part III of the Unified Parkinson’s Disease Rating Scale (UPDRS) (F(2,6) = 12.9, p = .73). Conclusion Despite a broad range of project methodologies and a diverse group of subjects, little sociodemographic or clinical heterogeneity was observed between longitudinal DLB projects. Interrogation of subject‐level data, rather than descriptive data for each project, and more detailed clinical data on participants would allow further examination of heterogeneity. This could support harmonisation of individual DLB datasets to create a single global DLB cohort.
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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 | 0.093 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".