Subjective memory complaints in Latin American older adults: Prevalence and risk factors
Bibliographic record
Abstract
BackgroundSubjective memory complaints (SMC) are linked to an increased risk of neurocognitive disorders (NCD).ObjectiveTo estimate the prevalence of SMC and their association with sociodemographic and clinical factors in 3285 older adults (OA) from ten Latin American and Caribbean (LAC) countries.MethodThis population-based analysis used secondary data from an international multicenter study on NCD prevalence during the COVID-19 pandemic. Cognitively healthy participants were identified based on clinical criteria, cognitive assessments, and expert consensus. Participants were categorized as with (WSMC; n = 602) or without SMC (NSMC; n = 2683). Sociodemographic and clinical variables were recorded. Cognitive performance was assessed using the Montreal Cognitive Assessment-Short Version (MoCA-T), depressive symptoms with the 15-item Geriatric Depression Scale (GDS-15), and functional decline with the Eight-Item Informant Interview (AD8). Mean difference analyses and logistic regressions were performed.ResultsThe regional prevalence of SMC was 18.33%, ranging from 11.59% in Guatemala to 26.30% in Peru. OA with SMC showed lower education, poorer cognitive performance, and higher rates of anxiety, falls, and fractures. Regression models revealed significant associations between SMC and lower education (p < 0.001), emotional distress (p < 0.001), age (p = 0.024), anxiety (p = 0.017), infrequent and occasional falls (p = 0.017; p = 0.002), and fractures (p = 0.028).ConclusionsSMC are prevalent among LAC older adults and are associated with multiple risk factors, highlighting their public health relevance and potential as early indicators of NCD risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".