Prevalence of Depression, Anxiety and Cognitive Impairment in Patients with Type 2 Diabetes from the Central Part of Romania
Bibliographic record
Abstract
Abstract Objective : The aim of this study was to assess the prevalence of depression, anxiety and cognitive impairment in patients with type 2 diabetes (T2D). Material and methods : We conducted a cross-sectional study in patients with T2D. Depression and anxiety were assessed by questionnaires (PHQ-9, CES-D and GAD-7 respectively), cognitive function by the MoCA test. Additionally, 503 patients’ clinic charts were separately analyzed in order to compare the data recorded in the charts with that resulted from the active assessment. Results : In the screening study 216 subjects with T2D were included (62.2 ± 7.8 years old). 34.3% of them had depression and 7.4% presented major depression. 44.9% of patients with T2D had anxiety (9.2% major anxiety) and this was highly correlated with depression (OR: 21.139, 95%CI: 9.767-45.751; p<0.0001). Women had significantly higher prevalence of depression and anxiety compared to men (42.1% vs. 21.7%; p: 0.0021 and 51.1% vs. 34.9%; p: 0.02), but severe depression was similar between genders (9.0% vs. 4.8%; p: 0.29). Significantly more patients had depression and anxiety than recorded in their charts (34.3% vs. 13.9% and 44.9% vs. 9.3%, respectively; p<0.0001 for both). 69.0% of T2D patients had mild, 6.0% had moderate and none had severe cognitive dysfunction, respectively. Significantly more patients with depression and anxiety had mild and moderate cognitive impairment (p: 0.03 and p: 0.04, respectively). Conclusions : Patients with T2D had a high prevalence of comorbid depression, anxiety and cognitive impairment. Depression and anxiety were significantly more frequent in women. These conditions were under-evaluated and/or under-reported.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".