Recurrent Subthreshold Depression in Type 2 Diabetes: An Important Risk Factor for Poor Health Outcomes
Bibliographic record
Abstract
OBJECTIVE To evaluate the association between recurrent subthreshold depressive episodes and functioning in a prospective community sample of people with type 2 diabetes. RESEARCH DESIGN AND METHODS A prospective community study in Quebec, Canada, was carried out between 2008 and 2013 (n = 1,064). Five yearly follow-up assessments (telephone interviews) were conducted. Baseline and the first three follow-up assessments were used to identify recurrent subthreshold depressive episodes (Patient Health Questionnaire [PHQ]-9). Functioning (World Health Organization Disability Assessment Schedule II [WHODAS-II]) and health-related quality of life (Centers for Disease Control and Prevention [CDC] unhealthy days) at 4- and 5-year follow-up assessments were the outcome measures. RESULTS Nearly half of the participants suffered from at least one episode of subthreshold depressive symptoms. After adjusting for potentially confounding factors, the risk of poor functioning/impaired health-related quality of life was nearly three times higher (relative risk = 2.86) for participants with four subthreshold depressive episodes compared with participants with no/minimal depression. Results suggest a dose-response relationship: the risk of poor functioning/impaired health-related quality of life increased with the number of recurrent subthreshold depressive episodes even after controlling for potentially confounding variables (significant linear trend, P < 0.001). CONCLUSIONS Recurrent subthreshold depressive symptoms might be an important risk factor for poor health outcomes in type 2 diabetes. Early identification, monitoring, and treatment of recurrent subthreshold depressive symptoms might improve functioning and quality of life in people with type 2 diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".