54-PUB: Exploring Categories of Controlled Cardiovascular Risk Factors in the Elderly—Insights from the Costa Rican Longevity and Healthy Aging Study
Bibliographic record
Abstract
Introduction: The prospective cohort Costa Rican Longevity and Healthy Aging Study (CRELES) offers comprehensive data on the lifestyle of elderly Costa Ricans enrolled in 2005, with survival status up to 2019. We aimed to describe the characteristics of the participants based on the number of controlled cardiovascular risk factors (CVRFs). Methodology: We recruited 1943 participants and categorized the number of CVRFs meeting targets from 0 to 5 (A1c, blood pressure, exercise, BMI, and smoking). Descriptively, we report proportions and means with SD for each variable categorized according to the number of controlled CVRFs. We used ANOVA for continuous and chi-square tests for categorical variables to establish differences between groups. Results: The distribution of CVRFs varied among participants, with 35.8% successfully managing three CVRFs. Notably, only 3.0% of participants attained control over all five CVRFs. The global prevalence of diabetes was 20.3%, exhibiting variations between groups. The mortality rate for all participants was 51.1% (see Table 1). Conclusions: The categorization of participants based on the number of CVRFs highlights variations in sociodemographic and clinical parameters across different subgroups. This stratification allows a better understanding of the cohort’s distribution of risk factors in the elderly. Disclosure J. Calvo Marin: Speaker's Bureau; Novo Nordisk, AstraZeneca. G. Torrealba-Acosta: None. K. Ernest-Suarez: Advisory Panel; Janssen Pharmaceuticals, Inc. Consultant; Janssen Pharmaceuticals, Inc. Speaker's Bureau; Janssen Pharmaceuticals, Inc. Advisory Panel; Pfizer Inc. Speaker's Bureau; Pfizer Inc. Advisory Panel; AstraZeneca. Consultant; AstraZeneca. Speaker's Bureau; AstraZeneca. Advisory Panel; Takeda Canada. Speaker's Bureau; Sandoz.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".