Rationale and Design of the “DIagnostic and Prognostic Precision Algorithm for behavioral variant Frontotemporal Dementia” (DIPPA-FTD) Study: A Study Aiming to Distinguish Early Stage Sporadic FTD from Late-Onset Primary Psychiatric Disorders
Bibliographic record
Abstract
BACKGROUND: The behavioral variant of frontotemporal dementia (bvFTD) is very heterogeneous in pathology, genetics, and disease course. Unlike Alzheimer's disease, reliable biomarkers are lacking and sporadic bvFTD is often misdiagnosed as a primary psychiatric disorder (PPD) due to overlapping clinical features. Current efforts to characterize and improve diagnostics are centered on the minority of genetic cases. OBJECTIVE: The multi-center study DIPPA-FTD aims to develop diagnostic and prognostic algorithms to help distinguish sporadic bvFTD from late-onset PPD in its earliest stages. METHODS: The prospective DIPPA-FTD study recruits participants with late-life behavioral changes, suspect for bvFTD or late-onset PPD diagnosis with a negative family history for FTD and/or amyotrophic lateral sclerosis. Subjects are invited to participate after diagnostic screening at participating memory clinics or recruited by referrals from psychiatric departments. At baseline visit, participants undergo neurological and psychiatric examination, questionnaires, neuropsychological tests, and brain imaging. Blood is obtained to investigate biomarkers. Patients are informed about brain donation programs. Follow-up takes place 10-14 months after baseline visit where all examinations are repeated. Results from the DIPPA-FTD study will be integrated in a data-driven approach to develop diagnostic and prognostic models. CONCLUSIONS: DIPPA-FTD will make an important contribution to early sporadic bvFTD identification. By recruiting subjects with ambiguous or prodromal diagnoses, our research strategy will allow the characterization of early disease stages that are not covered in current sporadic FTD research. Results will hopefully increase the ability to diagnose sporadic bvFTD in the early stage and predict progression rate, which is pivotal for patient stratification and trial design.
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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 | 0.099 | 0.066 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".