Multimorbidity in Canadians living in the community
Bibliographic record
Abstract
<h3>Objective</h3> To determine the mean number of chronic diseases in Canadians aged 45 to 85 years who are living in the community, and to characterize the association of multimorbidity with age, sex, and social position. <h3>Design</h3> An analysis of data from the Canadian Longitudinal Study on Aging. The number of self-reported chronic diseases was summed, and then the mean number of chronic health problems was standardized to the 2011 Canadian population. Analyses were conducted stratified on sex, age, individual income, household income, and education level. <h3>Setting</h3> Canada. <h3>Participants</h3> A total of 21 241 community-living Canadians aged 45 to 85 years. <h3>Main outcome measures</h3> Overall, 31 chronic diseases (self-reported from a list) were considered, as were risk factors that were not mental health conditions or acute in nature. Age, sex, education, and household and individual incomes were also self-reported. <h3>Results</h3> Multimorbidity was common, and the mean number of chronic illnesses was 3.1. Women had a higher number of chronic illnesses than men. Those with lower income and less education had more chronic conditions. The number of chronic conditions was strongly associated with age. The mean number of conditions was 2.1 in those aged 45 to 54; 2.9 in those 55 to 64; 3.8 in those aged 65 to 74, and 4.8 in those aged 75 and older (<i>P</i> < .05, ANOVA [analysis of variance]). <h3>Conclusion</h3> Multimorbidity is common in the Canadian population and is strongly related to age.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".