Cross-country variance in facial emotion recognition in presymptomatic and symptomatic behavioral variant frontotemporal dementia: Insights from the GENFI and ReDLat consortia
Bibliographic record
Abstract
INTRODUCTION We investigated international differences in facial emotion recognition (FER) across stages of frontotemporal dementia (FTD). Previous studies may have missed early decline by combining data and masking variations in FER across countries. METHODS An FER test was administered to 159 individuals with behavioral variant FTD, 521 presymptomatic pathogenic variant carriers, and 583 controls from 16 countries of residence. Linear mixed models assessed age, sex, education, and country effects on FER. Voxel-based morphometry examined neural correlates across countries. REULTS Country accounted for 18%–18.3% of FER variance in presymptomatic carriers and controls and 9.9% in individuals with behavioral variant of FTD (bvFTD). Cross-country differences interacted with the effects of sex, age, and education. Neural correlates involving the frontal lobe and basal ganglia were identified in individuals with bvFTD, but no cross-country differences were found. DISCUSSION These results underscore the need for culturally sensitive FER tools in research and clinical practice, especially as global multinational clinical trials emerge. Highlights Performance on a test for facial emotion recognition (FER) varies between countries. The percentage of variance is lower in the behavioral variant of frontotemporal dementia (bvFTD) compared to presymptomatic pathogenic variant carriers and healthy controls. Cross-country differences interacted with the effects of sex, age, and education. There were no differences in brain correlates of FER across countries.
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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 | 0.057 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".