Neuropsychological evaluation of cognitive decline in Latin America
Bibliographic record
Abstract
BACKGROUND: Investigating cognitive decline remains a challenge, especially due to regional differences. Latin American countries face disparities in access to healthcare, diagnostic tools, and treatments. Clinicians should actively inquire about cognitive decline complaints in patients, particularly those over 60 years of age. Cognitive and functional testing is crucial for evaluating patients with these complaints. METHOD: The implementation of quick, accessible, and easily scored screening tools that are less influenced by cultural and educational factors is recommended. RESULT: When cognitive decline is suspected, screening tools such as the 10-Point Cognitive Screener (10-CS) for patients and the Cognitive Change Questionnaire (QMC8) for caregivers are suggested; ideally, both should be administered. Additional cognitive assessment tools recommended for diagnosing mild cognitive impairment (MCI) and dementia include the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Addenbrooke's Cognitive Examination-Revised/III (ACE-R or ACE-III), Rowland Dementia Assessment Scale (RUDAS), and Brief Cognitive Screening Battery (BCSB). Functional assessment tools, such as the Pfeffer Functional Activities Questionnaire (FAQ), should also be considered. If concerning results are observed, further clinical investigation is advised, including differential diagnoses and the exclusion of reversible causes. Regular patient follow-ups are essential, with cognitive status reassessed every six months to monitor changes and educate patients on modifiable risk factors for dementia prevention. CONCLUSION: In this presentation, will be provide a recommendation on best practices in detecting cognitive decline with an adaptation to the Latin American culture and combination of available resources.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".