Cardiovascular risk in subjects over 55 years of age and cognitive performance after five years. NEDICES2-RISK study. Study protocol
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment and dementia have a high prevalence among the elderly and cause significant socio-economic impact. Any progress in their prevention can benefit millions of people. Current data indicate that cardiovascular risk (CVR) factors increase the risk of developing cognitive impairment and dementia. Using models to calculate CVR specific for the Spanish population can be useful for estimating the risk of cognitive deterioration since research on this topic is limited and predicting this risk is mainly based on outcomes in the Anglo-Saxon population. The aim of this study is to assess the relationship between CVR in the Spanish population, as calculated using the FRESCO (Función de Riesgo Española de acontecimientos Coronarios y Otros) and REGICOR (Registre Gironí del Cor) CVR tables, and the change in cognitive performance at a 5-year follow-up. METHODS: Design: Observational, analytic, prospective cohort study, with a 5-year follow-up. Ambit: Population. Population: Subjects 55 to 74 years of age, included in the NEDICES2 (2014-2017) cohort, who did not present dementia and had undergone the neuropsychological evaluation (N = 962). Variables: Exposure factors (CVR factors and estimated risk according to the CVR predictors by REGICOR and FRESCO), dependent variables (change in the score of the brief neuropsychological test in the study NEDICES2 five years after the first evaluation), and clinical and socio-demographic variables. Statistical analysis: Analysis of data quality. Descriptive analysis: socio-demographic and clinical variables of subjects. Bivariate analysis: relationship between basal CVR and change in neuropsychological tests. Multivariate analysis: relationship between basal CVR and change in neuropsychological tests adjusted by co-variables. Analysis and comparison of the reliable change in independent samples. DISCUSSION: The Spanish population can benefit from determining if individuals with high CVR, which is commonly detected in usual clinical practice, will present decreased cognitive performance compared to subjects with lower CVR. This study can affect how to address CVR factors and the design of effective prevention strategies for cognitive deterioration. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03925844.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".