Does Self-Reported BMI Modify the Association Between Stroke and Depressive Symptoms?
Bibliographic record
Abstract
ABSTRACT Background: Depressive symptoms are common in stroke survivors. While obesity has been associated with stroke and depression, its influence on the association between stroke and depressive symptoms is unknown. Methods: Cross-sectional data from 2015 to 2016 Canadian Community Health Survey was used. History of stroke was self-reported and our outcome of interest was depressive symptoms in the prior 2 weeks, measured using the 9-item Patient Health Questionnaire. Self-reported body mass index (BMI) was modeled as cubic spline terms to allow for nonlinear associations. We used multivariable logistic regression to evaluate the association between stroke and depressive symptoms and added an interaction term to evaluate the modifying effect of BMI. Results: Of the 47,521 participants, 694 (1.0%) had a stroke and 3314 (6.5%) had depressive symptoms. Those with stroke had a higher odds of depressive symptoms than those without (aOR = 3.13, 95% CI 2.48, 3.93). BMI did not modify the stroke-depressive symptoms association (P interaction = 0.242) despite the observed variation in stroke-depressive symptoms association across BMI categories,: normal BMI [18.5–25 kg/m2] (aOR † = 3.91, 95% CI 2.45, 6.11), overweight [25–30 kg/m2] (aOR † = 2.63, 95% CI 1.58, 4.20), and obese [>30 kg/m2] (aOR † = 2.76, 95% CI 1.92, 3.94). Similar results were found when depressive symptoms were modeled as a continuous measure. Conclusion: The association between stroke and depressive symptoms is not modified by BMI, needing additional work to understand the role of obesity on depression after stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".