Relationships Between Handgrip Strength Asymmetry And Multimorbidity In Canadian Adults Aged 40 Years And Older: Results From The Canadian Health Measures Survey
Bibliographic record
Abstract
Background: Hand-grip strength (HGS) is a convenient, valid, and reliable measure of overall muscle strength that is significantly related to current and future health. HGS asymmetry is an additional aspect of muscle function that can also be measured using handheld dynamometry. The aim of this study was to examine associations between HGS asymmetry and multimorbidity in a nationally representative sample of Canadian adults aged 40 years or older. Methods: A secondary analysis of cross-sectional data was performed on adults aged 40-79 years from six cycles (2007- 2017) of the Canadian Health Measures Survey (CHMS). HGS was assessed using handheld dynamometry, with HGS asymmetry calculated as the ratio of the maximum scores for the strongest and weakest hands. Multimorbidity was defined as the presence of two or more of the following chronic conditions: arthritis, mental disorder (mood disorder and/or anxiety), asthma, diabetes mellitus, heart disease, chronic obstructive pulmonary disease (COPD), cancer, or stroke. Crude and covariate-adjusted logistic regression models were used to quantify the relationship between HGS asymmetry and multimorbidity. Results: HGS asymmetry was significantly associated with multimorbidity in Canadian adults. Relative to individuals without asymmetry (<11% asymmetry), adults with ≥21% asymmetry had 1.34 greater odds for multimorbidity (95% CI: 1.01–1.79) after adjustment for covariates. When stratified by sex, significant associations were found only for women. Women with ≥21% HGS asymmetry had 1.55 (95% CI: 1.09–2.20) greater odds for multimorbidity. Conclusion: These findings indicate that HGS asymmetry is associated with multimorbidity in Canadian women and suggest that HGS asymmetry has potential utility for clinical screening and population health surveillance for women at risk of multimorbidity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".