Depressive episodes, weight change, and incident diabetes in a Canadian community sample
Bibliographic record
Abstract
IntroductionDepression has consistently been associated with an increased risk of diabetes in recent meta-analyses. However, depression is a highly heterogeneous construct and people with specific symptoms of depression, such as weight gain and increased sleep, may be at a higher risk of diabetes.
 Objectives and ApproachThis work will compare incident diabetes in Ontario adults with recent depressive episodes that included symptoms of weight gain, weight loss, or no weight change and in those with no recent depressive episodes. Participants will be drawn from several waves of the Canadian Community Health Survey and the National Population Health Survey. Past 12-month depressive episodes and weight change during most recent or worst episodes was measured using the CIDI/CIDI short form. Time to incident diabetes will be ascertained through linkage with the Ontario Diabetes Database. Cox proportional hazards regression will assess diabetes incidence by depression and weight change characteristics.
 ResultsThis study will include 106 084 Ontario adults who participated in the Canadian Community Health Survey (2000/2001, 2002, 2003, 2012) and the National Population Health Survey (1996). Follow-up time will range from 4 to 19 years (until March 2017). Study covariates will include demographic and lifestyle factors, comorbidities, and health care use and will be extracted from the surveys above and from administrative health data. The dataset for this study is currently being prepared by the Institute for Clinical Evaluative Sciences (ICES) and the findings of this analysis will be presented at this conference.
 Conclusion/ImplicationsThe results of this work will provide insight into who, among those with depression, is at highest risk of new-onset diabetes. These results will be relevant to the development of both personalized and population-level diabetes screening and prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".