Dementia diagnosis inequality between high‐income countries and Brazil: how challenging the diagnosis can be
Bibliographic record
Abstract
BACKGROUND: Early diagnosis is essential for effective dementia management, but LMICs, including Brazil, face a significant disparity in terms of resources for diagnosis compared to high-income countries. Therefore, we aim to assess diagnostic methods and clinical practice tools available in specialized dementia care centers across Brazil. METHODS: We developed an online survey to get information about the current infrastructure, cognitive tests utilized, the availability of cerebrospinal fluid (CSF) and imaging biomarkers, and patients' sociodemographic data. This survey gathered responses in July, 2024 from neurologists affiliated with the Brazilian Academy of Neurology who work in centers specialized in dementia management. RESULTS: Neurologists from 24 outpatient clinics across 10 Brazilian states participated. Most clinics (87%) are associated with the Public Health System. Regarding research infrastructure, 39.1% lack a research database, and clinical data remain in medical records. All settings record age, sex, and education, but the settings often omit ethnicity (67.0%) and socioeconomic status (27.0%). Clinical dementia diagnoses are registered electronically in 78.0% of centers. Cognitive assessment relies on the Mini-Mental State Examination (100%), the Montreal Cognitive Assessment (66.7%), and the Brief Cognitive Screening Battery (54.2%). Functional assessment relies on the Pfeffer Questionnaire in 33.3% and on the KATZ Scale in 20.8% of settings. Neuropsychiatric evaluations occur in 58.3%, especially based on the Geriatric Depression Scale. Only 29.2% of centers assess dementia staging, and Clinical Dementia Rating is the primary tool. Neuropsychological and functional evaluations are unavailable in 33.3% and 66.7% of settings, respectively. Only 29.2% assess Alzheimer's disease CSF biomarkers, differing from the routine laboratory tests (100%) and routine CSF analysis (79.2%). Brain MRI is universally available, but PET-FDG (25%) and PET-amyloid (12.5%) are scarce. Genetic testing is unavailable in 70.8% of clinics. CONCLUSION: The survey highlights significant heterogeneity and gaps in dementia diagnostic process in Brazil. Clinical and cognitive assessment dominate the diagnostic practice, contrasting with the latest criteria emphasizing biomarkers. Efforts to harmonize dementia diagnostic practices, standardize data collection and expand access to advanced diagnostic tools will enhance the dementia care landscape.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".