A cognitive composite for genetic frontotemporal dementia: GENFI‐cog
Bibliographic record
Abstract
Abstract Background Development of endpoints for clinical trials in frontotemporal dementia (FTD) is increasingly urgent. In other neurodegenerative diseases composite scores are often used as outcome measures but are, as of yet, lacking in FTD. The aim of this study was to create gene‐specific cognitive composite scores for MAPT , GRN and C9orf72 mutation carriers and provide recommendations on recruitment and trial duration. Method 69 C9orf72 , 41 GRN , 28 MAPT mutation carriers with a CDR® plus NACC‐FTLD global score ≥0.5 and 275 controls completed a neuropsychological battery covering five cognitive domains. Logistic regression was used to identify the combination of tests that discriminated best between mutation carrier groups and controls. Weighted averages of the test scores in the models were calculated based on the regression coefficients (GENFI‐cog). Sample size estimates were calculated for individual tests and composite. The treatment effect was estimated as the mean difference between CDR® plus NACC‐FTLD 0.5 and 1 groups. Time‐to‐event analysis was used to determine the fraction of patients within GENFI that converted from CDR® plus NACC‐FTLD 0.5 to ≥1. Result The most sensitive model in C9orf72 mutation carriers included a combination of executive, social cognitive and visuoconstructive tests (Table 1). A combination of executive, social cognitive, semantic and memory tests was most sensitive in GRN mutation carriers, and a combination of social cognitive, attention, semantic and memory tests in MAPT mutation carriers, resulted in the most sensitive model. The estimated sample size to detect a treatment effect was lower for the composite than for most individual tests (Table 2). A Kaplan‐Meier curve (Figure 1) showed that after three years 50% of individuals convert from CDR® plus NACC‐FTLD global score 0.5 to 1 or more. Conclusion We created gene‐specific cognitive composite scores for C9orf72, GRN and MAPT mutation carriers (GENFI‐cog) which resulted in substantially lower estimated sample sizes to detect a treatment effect than most individual cognitive tests. Only 50% of patients with a CDR® plus NACC‐FTLD of 0.5 convert to ≥1 in a three‐year period. GENFI‐cog has potential as a cognitive endpoint for upcoming trials and the results from this study provide important information concerning trial duration and sample sizes.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".