Cognitive, clinical and genetic characterization of dementia in Cali, Colombia: A pilot study in a diverse population
Bibliographic record
Abstract
Abstract Background The aging population and the prevalence of dementia are rapidly growing in Latin America. People with dementia are underdiagnosed and not well characterized in this region. The scenario is complicated by limited options for culturally proper testing and Latinos' socio‐biological and phenotypic diversity, which affect dementia presentation. In Cali, frontotemporal dementia (FTD) is misdiagnosed as a psychiatric disorder in >70% of cases. Delay in the accurate diagnosis and misdiagnosis of dementia affects genetic counseling, treatment, prognosis, and caregiver burden. Furthermore, most of the discovery efforts in genetics are based on data from populations of European ancestry. This bias in representation can exacerbate existing disease and healthcare disparities. Innovative technology for neuropsychological assessment could help make an early and accurate diagnosis. Genetic analysis is novel in this region, and the proposed work lays the basis for the genomic studies we advocate for. Method We will evaluate the accuracy of the TabCAT cognitive tests for the identification of cognitive impairment in participants previously diagnosed with FTD (N = 20), AD (N = 20), and healthy controls (N = 40). We also will assess the NPI. In addition, we will collect family history information for neurodegenerative diseases, genome sequencing of the sample. Result We hypothesize that the TabCAT evaluation will show sensitivity and specificity >.80 for detecting cognitive impairment, exceeding the accuracy of standard cognitive screenings, such as the Montreal Cognitive Assessment (MoCA). The assessment will be similarly accurate for detecting cognitive impairment in AD and bvFTD, each separately compared to controls. For participants with bvFTD, the NPI will improve the classification of bvFTD from controls and AD, above and beyond the cognitive tests. We hypothesize that 10‐20% of the participants will have a family history of dementia and be identified with genetic risk for neurodegenerative diseases. Conclusion This pilot lays the groundwork to establish a new site for ReDLat, a consortium of multiple partners to expand dementia research in Latin America and the Caribbean. Assessing this underrepresented population, we will contribute to an accurate characterization of dementia in Colombia, which will improve dementia diagnosis and reduce health inequities in this region.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".