Differences in sex distribution between genetic and sporadic FTD
Bibliographic record
Abstract
Abstract Background The reported sex distribution differs between frontotemporal dementia (FTD) cohorts. Possible explanations are the evolving clinical criteria of FTD and its subtypes and the discovery of FTD causal genetic mutations that have resulted in variable findings. Our aim was to determine the sex distribution in a large international retrospective cohort of sporadic and genetic FTD. Method We included patients with probable and definite behavioural variant frontotemporal dementia (bvFTD), non‐fluent variant primary progressive aphasia (nfvPPA), semantic variant primary progressive aphasia (svPPA) and right temporal variant frontotemporal dementia (rtvFTD) from the Amsterdam Dementia Cohort, the Montreal Neurological Institute Cohort, the University of Ulm and Technical University of Munich Cohort (part of the German Consortium of Frontotemporal Lobal Degeneration), the Policlinico Milan Cohort and the Sydney FRONTIER Cohort. We compared sex distribution between genetic and sporadic FTD using χ2 tests. Result A total of 910 subjects were included (56.3% male), of whom 654 had bvFTD, 99 nfvPPA, 117 svPPA and 40 rtvFTD. Of these, 215 had genetic FTD and the sex distribution was equal (51.2% male), which did not differ significantly from sporadic FTD (57.8% male, χ2 p=0.081). In the sporadic bvFTD subgroup, we found a male predominance (61.6% males compared to 52.9% males in the bvFTD genetic group, χ 2 p=0.04). No sex distribution differences between sporadic and genetic cases were found in the other clinical FTD subgroups (all p>0.05). Conclusion Differences in sex distribution between genetic and sporadic behavioural variant of FTD may provide important clues for its differential pathogenesis and warrants further research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".