Bibliographic record
Abstract
BACKGROUND: Although diabetes mellitus has a strong association with the presence of depression, it is unclear whether diabetes itself increases the risk of developing depression. The objective of our study was to evaluate whether people with diabetes have a greater incidence of depression than those without diabetes. METHODS: We conducted a population-based retrospective cohort study using the administrative databases of Saskatchewan Health from 1989 to 2001. People older than 20 years with newly identified type 2 diabetes were identified by means of diagnostic codes and prescription records and compared with a nondiabetic cohort. Depression was ascertained via diagnostic codes and prescriptions for antidepressants. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for age, sex, frequency of visits to physicians and presence of comorbidities. RESULTS: We identified 31 635 people with diabetes and 57 141 without. Those with diabetes were older (61.4 v. 46.8 yr; p < 0.001), were more likely to be male (55.4% v. 49.8%; p < 0.001) and had more physician visits during the year after their index date (mean 14.5 v. 5.9; p < 0.001). The incidence of new-onset depression was similar in both groups (6.5 v. 6.6 per 1000 person-years among people with and without diabetes, respectively). Similarity of risk persisted after controlling for age, sex, number of physician visits and presence of prespecified comorbidities (adjusted HR 1.04, 95% CI 0.94- 1.15). Other chronic conditions such as arthritis (HR 1.18) and stroke (HR 1.73) were associated with the onset of depression. INTERPRETATION: Using a large, population-based administrative cohort, we found little evidence that type 2 diabetes increases the risk of depression once comorbid diseases and the burden of diabetes complications were accounted for.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".