An introduction to GENFI and the development of novel behavioural and cognitive measures for genetic FTD
Bibliographic record
Abstract
Abstract Background The Genetic FTD Initiative (GENFI) is the largest familial FTD cohort study worldwide and over its initial ten years has created a network of nearly 30 research centres across Europe and Canada, and a cohort of over 1000 participants. Method GENFI was established to develop robust biomarkers that could be used in clinical trials. In terms of clinical and cognitive measures, the study has used a set of novel symptom scales as well as a detailed neuropsychological battery, all harmonized across the network, to identify new rating scales and composite measures. Result The study has characterized behavioural and neuropsychological changes across the different genetic forms of FTD, highlighting the earliest prodromal changes that occur in different domains, including emotion recognition, semantic knowledge and executive dysfunction. GENFI data has also been used to validate two core clinical/cognitive scales, highlighting their utility for clinical trials in genetic FTD: the CDR plus NACC FTLD and the FTD Rating Scale (FRS). An extension to the CDR plus NACC FTLD with motor and neuropsychiatric domains highlights the contribution of symptoms outside of classical behavioural and language domains to accurately measuring disease progression. The study has also combined neuropsychological measures to create a cognitive composite measure (GENFI‐Cog), individualized to each genetic group, which greatly reduces sample sizes required for clinical trials. Conclusion The GENFI study has developed and validated a number of clinical and cognitive measures that are now being implemented in clinical trials for novel therapeutics in familial FTD. Future studies in GENFI will help to refine these even further.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".