The Prevalence and Incidence of Frontotemporal Dementia: a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Population-based prevalence and incidence studies are essential for understanding the burden of frontotemporal dementia (FTD). METHODS: The MEDLINE and EMBASE databases were searched to identify population-based publications from 1985 to 2012, addressing the incidence and/or prevalence of FTD. References of included articles and prior systematic reviews were searched for additional studies. Two reviewers screened all abstracts and full-text reviews, abstracted data and performed quality assessments. RESULTS: Twenty-six studies were included. Methodological limitations led to wide ranges in the estimates for prevalence (point prevalence 0.01-4.6 per 1000 persons; period prevalence 0.16-31.04 per 1000 persons) and incidence (0.0-0.3 per 1000 person-years). FTD accounted for an average of 2.7% (range 0-9.1%) of all dementia cases among prevalence studies that included subjects 65 and older compared to 10.2% (range 2.8-15.7%) in studies restricted to those aged less than 65. The cumulative numbers of male (373 [52.5%]) and female (338 [47.5%]) cases from studies reporting this information were nearly equal (p=0.18). The behavioural variant FTD (bvFTD) was almost four times as common as the primary progressive aphasias. CONCLUSIONS: Population-based estimates for the epidemiology of FTD varied widely in the included studies. Refinements in the diagnostic process, possibly by the use of validated biomarkers or limiting case ascertainment to specialty services, are needed to obtain more precise estimates of the prevalence and incidence of FTD.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".