Evaluation of the prevalence of the most common psychiatric disorders in patients with type 2 diabetes mellitus using the patient health questionnaire: results of the cross-sectional “DIA2PSI” study
Bibliographic record
Abstract
AIMS: Common Psychiatric Disorders (CPDs) are associated with the development of overweight and obesity, the strongest risk factors for the onset and maintenance of Type 2 Diabetes mellitus (T2D). To the best of our knowledge, this is the first study to assess the prevalence of CPDs in patients with T2D in Italy. METHODS: This is a monocentric cross-sectional study; n = 184 T2D patients were screened for CPDs using the Patient Health Questionnaire (PHQ). Primary outcome was to evaluate the prevalence of CPDs. To assess association between CPDs and risk factors, we have utilized univariable logistic regression models. RESULTS: . The 42.9% tested positive for one or more mental disorders, 25.6% for depression. Patients with higher BMI (p = 0.04) had an increased likelihood of testing positive to the PHQ. Patients who had implemented lifestyle changes (p < 0.01) and were aware that mental health is linked to body health (p = 0.07) had a reduction in the likelihood of testing positive. CONCLUSIONS: Prevalence of CPDs in T2D patients is higher than in the general population. Since CPDs favor the onset and subsistence of T2D, integrated diabetic-psychiatric therapy is required for improvement or remission of T2D in patients with comorbid CPDs.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".