A prospective long‐term study of TDP‐43 pathology in Alzheimer’s disease aggression
Bibliographic record
Abstract
Abstract Background Neuropsychiatric symptoms (NPS) in dementia is a major challenge for the health‐care system with a lack of effective treatment strategies. Especially agitation and aggression are pivotal as they represent a large part of the health and social care costs of dementia. But the mechanisms of agitation and aggression are still largely unknown. Comorbid TDP‐43 pathology is common in AD, and here we wanted to explore the association of TDP‐43 pathology and agitation and other NPS in Alzheimer’s disease (AD) and Lewy body dementia (LBD). Method From a cohort of 223 patients with dementia who were followed annually for up to 12 years with the Neuropsychiatric Inventory to assess agitation, aggression and psychotic symptoms, post‐mortem standardized semi‐quantitative assessments of TDP‐43 were performed in 47 patients. Result In AD (n = 31) patients with aggression had more severe TDP‐43 pathology than those without aggression (p < 0.05). In LBD (n = 16) no significant associations between pathological scores and aggression or psychotic symptoms were found. Conclusion TDP‐43 may be a possible mechanism behind aggression in AD and its relation to neuropsychiatric symptom in AD needs further investigation. The description of Limbic‐predominant age‐related TDP‐43 encephalopathy (LATE) as a separate disease entity highlights the importance of limbic TDP‐43‐pathology in dementia, and TDP‐43 is a potential drug‐target. Cannabidiol (CBD) has shown effect on TDP‐43 pathways, and preliminary studies have suggested that cannabinoids may improve pain related to neuropathy, spasticity, and psychotic symptoms, which are all associated with aggression in dementia. We are planning Cannabidiol in Alzheimer’s disease Aggression (CanADA); a randomized control trial from 3 centers in Norway.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".