Prevalence of FTD clinical diagnostic features in patients with TDP‐43 pathology
Bibliographic record
Abstract
Abstract Background Frontotemporal dementia (FTD) is a heterogeneous syndrome which includes three main clinical subtypes, behavioural variant FTD (bvFTD) and the non‐fluent and semantic variants of primary progressive aphasia (nfvPPA, svPPA). The pathology underlying clinical FTD, referred to as frontotemporal lobar degeneration (FTLD), is also heterogenous with transactive response DNA binding protein Mw 43 (FTLD‐TDP) present in ∼50%. In this study we investigate the proportion of FTLD‐TDP cases that fulfil diagnostic criteria for each of the clinical FTD subtypes. Method We retrospectively reviewed charts of patients with autopsy proven FTLD‐TDP. Patients with additional pathology, (e.g. Alzheimer’s disease, AD), were excluded. We extracted clinical data regarding behaviour, language and cognition, and compared these against the current FTD clinical diagnostic criteria. Result Fifteen of 24 patients (62.5%) with autopsy‐confirmed FTLD‐TDP pathology met diagnostic criteria for possible bvFTD (n=9), nfvPPA (n=3) or svPPA(n=3). The clinical diagnosis of the nine patients who did not match criteria for any of the three FTD syndromes included FTD with motor neuron disease (n=5), AD type dementia (n=3), progressive supranuclear palsy (n=1), corticobasal syndrome (n=1) and FTD with Parkinsonism linked to chromosome 17 (n=1). Patients with motor neuron disease developed 1 (n=2) or 2 (n=3) diagnostic features of bvFTD. Among the bvFTD diagnostic features, executive dysfunction was the commonest (79.2%), followed by apathy (37.5%) and disinhibition (33.3%). 70.8% of all patients and 42.9% of bvFTD patients developed clinical features of aphasia. Cognitive screening tests revealed a high prevalence of impaired short‐term recall (79.2%) and language dysfunction (83.3%) irrespective of the clinical diagnosis. Conclusion bvFTD is the commonest presentation of FTLD‐TDP; however, a significant proportion of patients do not match diagnostic criteria for FTD. Additionally, the high prevalence of impaired short‐term recall suggests it may not be practical to use this as an exclusionary criteria for FTD. We recognize the selection bias in our sample which was derived exclusively from a dementia clinic population.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".