Clinical Presentation of Prodromal Frontotemporal Dementia
Bibliographic record
Abstract
BACKGROUND: Misrecognition of symptoms in the early stages of frontotemporal dementia (FTD) frequently contributes to diagnostic delay. Three frameworks have been proposed for the clinical identification of prodromal FTD: (1) cognitive profiling, (2) the presence of behavioral/psychiatric symptoms in the absence of memory complaints, and (3) a combined approach of cognitive, behavioral, and neuroimaging features. OBJECTIVE: To evaluate current conceptual frameworks for the clinical recognition of prodromal FTD with current empirical evidence. METHOD: We performed a comprehensive PsychINFO and MEDLINE database search to identify articles investigating the prodromal symptoms of FTD. CONCLUSIONS: The 3 frameworks capture important aspects of the clinical picture of prodromal FTD but require further refinement. The prodromal stage of FTD is characterized by both cognitive and behavioral features. Diagnostic accuracy will likely be improved by considering a combination of cognitive and behavioral features, because some features overlap with prodromes for Alzheimer's disease and vascular dementia.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".