Psychiatric symptoms in the early detection and differential diagnosis of FTD
Bibliographic record
Abstract
Abstract Background The early symptoms of behavioral‐variant frontotemporal dementia (bvFTD) have considerable overlap with the common primary psychiatric disorders like major depression and psychotic disorders. Consequently, about 50% are diagnosed with a psychiatric disorder before the identification of bvFTD. While this can be due to inadequate recognition of bvFTD features and lack of expertise, the diagnostic effort is also complicated by the fact that psychiatric symptoms like delusions and hallucinations are sometimes the prodrome to bvFTD—particularly in familial cases. Whereas the reliability of Alzheimer’s disease diagnoses has improved with the advent of molecular biomarkers, the diagnosis of bvFTD remains primarily based on clinical assessment and neuroimaging. The need for a systematic approach to distinguish bvFTD from psychiatric disorders is addressed in recommendations recently developed by an internal panel of experts, the Neuropsychiatric International Consortium for FTD, and are reviewed in this presentation. Method The Neuropsychiatric International Consortium for FTD was convened to develop recommendations for a diagnosis approach for identifying bvFTD in individuals with midlife‐onset (>45 years) behavior change. The consensus process included a systematic review of the literature to identify best practices, effective clinical measures and the utility of brain imaging. Result Differential diagnosis of bvFTD from psychiatric disorders is facilitated by comprehensive and chronologically‐ordered history, family history of neurodegenerative or neuromuscular disease, use of formal diagnostic criteria for bvFTD and for psychiatric disorders, use of bedside measures of general cognition and social behavior, psychiatric scales and brain imaging. These have been incorporated into an algorithm to facilitate use of the recommendations. Conclusion A systematic approach to the clinical examination can be facilitate the differential of bvFTD from primary psychiatric disorders. This is valuable for providing timely diagnosis, and early counseling and treatment planning to patients, for facilitating testing of novel treatments in clinical trials—and will also facilitate proper treatment for patients with primary and secondary psychiatric states.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".