Incidence and factors associated with new depressive episodes in adults with newly treated type 2 diabetes: A cohort study
Bibliographic record
Abstract
AIMS: Several methods are available to help identify people with depression; however, there is little guidance on when to start screening. This study estimated the incidence of new depressive episodes and identified factors associated with onset in adults with newly treated type 2 diabetes. METHODS: Administrative health data from Alberta, Canada was used to identify people starting metformin between April 2011 and March 2015. People with a history of depression before metformin initiation were excluded. Person-time analysis was used to calculate the incidence rate of new depressive episodes over the next 3 years, stratified by sex, age, and year. Multivariable logistic regression was used to identify factors independently associated with a new depressive episode. RESULTS: 42,694 adults initiated metformin; mean age 56 years, 38 % female. A new depressive episode occurred in 2752 (6 %) individuals, mean time to onset was 1.4 years and overall incidence rate was 22.3/1000 person-years. Factors associated with a new depressive episode were female sex, younger age, previous mental health conditions, frequent healthcare utilization, and multiple comorbid conditions. CONCLUSIONS: Screening for depression should begin within 1-2 years of metformin initiation and focus on females, those < 55 years old, those with a history of mental health conditions, and those with multiple comorbid conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".