[P4–418]: SEX DIFFERENCES IN THE PREVALENCE OF GENETIC MUTATIONS IN FTD AND ALS: A META‐ANALYSIS
Bibliographic record
Abstract
Sex differences have been identified in the prevalence of amyotrophic lateral sclerosis (ALS), whereas these findings in frontotemporal dementia (FTD) are mixed, suggesting biological and environmental factors may play a role in disease expression. However, sex differences in mutations that cause these disorders are largely unknown. Therefore, we conducted a meta-analysis of sex differences in the prevalence of mutations in the three most common genes that cause ALS and FTD, chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN), or microtubule associated protein tau (MAPT) in patients clinically diagnosed with these conditions. MEDLINE, EMBASE and PsycINFO databases were searched (inception to June 30, 2016). Studies of FTD or ALS patients that reported the number of men and women with and without mutations of interest were selected. Female to male pooled risk ratios (RR) and 95% confidence intervals (CI) for each mutation were calculated using random-effects models. Eighty-six studies of 21,067 patients were included. We found a higher prevalence of females with C9orf72-related ALS (RR 1.15, 95% CI 1.04 − 1.28), but no sex difference in C9orf72-related FTD (RR 0.95, 95% CI 0.81 − 1.12). We also found a higher prevalence of females with GRN-related FTD (RR 1.33, 95% CI 1.09 − 1.62) but no sex difference in MAPT-related FTD (RR 1.21, 95% CI 0.95 − 1.55). Higher female prevalence of C9orf72 hexanucleotide repeat expansions in ALS and GRN mutations in FTD suggest that sex-related risk factors might moderate C9orf72 and GRN-mediated phenotypic expression.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.053 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".