Dementia diagnosis inequality between high-income countries and Brazil: how challenging the diagnosis can be
Bibliographic record
Abstract
Background: Early diagnosis is crucial for effective dementia management, but low- or middle-income countries, including Brazil, face disparities in diagnostic resources. Objectives: To assess diagnostic tools in specialized dementia care centers across Brazil. Methods: An online survey was conducted in July 2024, targeting medical specialists working in Brazilian dementia care centers. The survey collected data on infrastructure, cognitive tests, biomarker availability, and patients’ sociodemographic profiles. Results: Dementia specialists from 24 outpatient clinics across 10 states participated in this study. Most settings (87%) are part of the Public Health System. While patients’ age, sex, and education were consistently recorded, ethnicity (67%) and socioeconomic status (27%) were often omitted. Cognitive assessments rely heavily on the Mini-Mental State Examination (100%), Montreal Cognitive Assessment (66.7%), and Brief Cognitive Screening Battery (54.2%). Functional assessments rely on the Pfeffer Questionnaire (33.3%) and KATZ Scale (20.8%). Neuropsychiatric evaluations occur in 58.3%, mainly via the Geriatric Depression Scale. Only 29.2% assess dementia staging, and Clinical Dementia Rating is the primary tool. Neuropsychological and functional assessments were unavailable in 33.3 and 66.7% of clinics, respectively. CSF biomarkers were available in 29.2%; brain MRI was universal, while PET-FDG and PET-amyloid were rare. Discussion: Significant gaps and heterogeneity exist in dementia diagnostic practices in public facilities in Brazil. The predominance of cognitive screening over comprehensive evaluations underscores the need for standardized protocols. Conclusion: Expanding access to advanced tools and standardizing assessments are essential for improving dementia care in Brazil.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".