Estimate of Dementia Prevalence in a Community Sample from São Paulo, Brazil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: To estimate dementia prevalence and describe the etiology of dementia in a community sample from the city of São Paulo, Brazil. METHODS: A sample of subjects older than 60 years was screened for dementia in the first phase. During the second phase, the diagnostic workup included a structured interview, physical and neurological examination, laboratory exams, a brain scan, and DSM-IV criteria diagnosis. RESULTS: Mean age was 71.5 years (n = 1,563) and 58.3% had up to 4 years of schooling (68.7% female). Dementia was diagnosed in 107 subjects with an observed prevalence of 6.8%. The estimate of dementia prevalence was 12.9%, considering design effect, nonresponse during the community phase, and positive and negative predictive values. Alzheimer's disease was the most frequent cause of dementia (59.8%), followed by vascular dementia (15.9%). Older age and illiteracy were significantly associated with dementia. CONCLUSIONS: The estimate of dementia prevalence was higher than previously reported in Brazil, with Alzheimer's disease and vascular dementia being the most frequent causes of dementia. Dementia prevalence in Brazil and in other Latin American countries should be addressed by additional studies to confirm these higher dementia rates which might have a sizable impact on countries' health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it