Describing the Profile of Individuals at Heightened Risk for Cardiometabolic Multimorbidity: A Secondary Analysis of the Canadian Longitudinal Study on Aging Data
Bibliographic record
Abstract
Purpose Develop a parsimonious model of individuals at heightened-risk for 3-year cardiometabolic multimorbidity (CM) onset. Design An observational, secondary analysis of Canadian Longitudinal Study on Aging (CLSA) data. Setting CLSA is a national cohort study in Canada. Baseline data were collected between 2010-2015, and follow-up data were collected between 2015-2018. Subjects CLSA included community-dwelling adults aged 45-85 at recruitment from across Canada. Measures Health conditions: stroke, heart disease or heart attack and diabetes. Personal factors: age, sex, marital status, household income, education, and ethnicity. Environmental factors: social support, personal assistance, and location of residence. CM cases: at least two of stroke, heart disease and diabetes at follow-up assessment. Analysis Hierarchical logistic regression analyses with backwards elimination procedures were used to develop a parsimonious prediction model. Results The sample consisted of 41 841 individuals, representing a weighted population of 13 741 119. The population had a mean age of 62.3 years (SD = 10.1), was 53% female, predominantly married or in common-law relationships (77%), post-secondary graduates (61%), white (95%), and lived in an urban area (81%). Males (OR:1.93, 95%CI:1.65-2.25, P < 0.001), ≥65 years (OR:1.51, 95%CI:1.29-1.76), P < 0.001), who had stroke (OR:20.09, 95%CI:12.88-30.35, P < 0.001), heart disease (OR:15.55, 95%CI:12.60-19.26, P < 0.001), or diabetes (OR:12.57, 95%CI:10.37-15.31, P < 0.001), not completed post-secondary (OR:1.30, 95%CI:1.04-1.61, P = 0.017), income of <50k (OR:1.29, 95%CI:1.10-1.52, P = 0.002), and received home care (OR:1.56, 95%CI:1.17-2.04, P = 0.002) were at heightened risk of CM. Conclusions Developing a profile of high-risk individuals may enhance the efficiency of CM prevention and reduce disease onset. Critical limitations include the CLSA exclusion criteria, and the small proportion of minoritized individuals that restrict generalizability in these populations.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".