Neuropsychiatric symptoms in genetic frontotemporal dementia: developing a new module for Clinical Rating Scales
Bibliographic record
Abstract
BACKGROUND: Current clinical rating scales in frontotemporal dementia (FTD) often do not incorporate neuropsychiatric features and may therefore inadequately measure disease stage. METHODS: 832 participants from the Genetic FTD Initiative (GENFI) were recruited: 522 mutation carriers and 310 mutation-negative controls. The standardised GENFI clinical questionnaire assessed the frequency and severity of 14 neuropsychiatric symptoms: visual, auditory, and tactile hallucinations, delusions, depression, anxiety, irritability/lability, agitation/aggression, euphoria/elation, aberrant motor behaviour, hypersexuality, hyperreligiosity, impaired sleep, and altered sense of humour. A principal component analysis (PCA) was performed to identify key groupings of neuropsychiatric and behavioural items in order to create a new neuropsychiatric module that could be used as an addition to the Clinical Dementia Rating (CDR) plus National Alzheimer's Coordinating Center Behaviour and Language Domains (NACC FTLD) rating scale. RESULTS: ) compared with 24.5% of controls. Anxiety and depression were the most common in all genetic groups but fluctuated longitudinally and loaded separately in the PCA. Hallucinations and delusions loaded together, with the remaining neuropsychiatric symptoms loading with the core behavioural features of FTD. These results suggest using a single 'psychosis' neuropsychiatric module consisting of hallucinations and delusions. Adding this to the CDR plus NACC FTLD, called the CDR plus NACC FTLD-N, leads to a number of participants being scored more severely, including those who were previously considered asymptomatic now being scored as prodromal. CONCLUSIONS: Neuropsychiatric symptoms occur in mutation carriers at all disease stages across all three genetic groups. However, only psychosis features provided additional staging benefit to the CDR plus NACC FTLD. Inclusion of these features brings us closer to optimising the rating scale for use in trials.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".