The Diagnostic Challenges of Late-onset Neuropsychiatric Symptoms and Early-onset Dementia: A Clinical and Neuropathological Case Study
Bibliographic record
Abstract
The emergence of new-onset neuropsychiatric symptoms in middle age presents a diagnostic challenge, particularly when differentiating between a primary psychiatric disorder and an early neurodegenerative disease. The discrepancy between bedside clinical diagnosis and subsequent neuropathological findings in such cases further highlights the difficulty of accurately predicting pathology, especially when there are no evident focal lesions or changes in brain volume. Here we present the case of a 59-year-old woman with inconclusive neuroimaging who exhibited pronounced neuropsychiatric and behavioral symptoms initially suggestive of a mood disorder, then of behavioral variant frontotemporal dementia. However, upon autopsy, we identified coexisting Lewy body disease pathology and tau-related changes, including argyrophilic grain disease and primary age-related tauopathy. This case illustrates the challenges encountered when diagnosing late-onset neuropsychiatric symptoms, emphasizes the link between such symptoms and early-onset dementia and argyrophilic grain disease, and contributes to our understanding of the impact of mixed neuropathology in this population. Accurate diagnosis is essential for the development of molecular-specific therapies and, as well as for accurate prognosis and enrollment in clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".