Cognitive Decline and Its Associated Factors in Patients with Major Depressive Disorder
Bibliographic record
Abstract
Background: Major Depressive Disorder (MDD) is a significant and common mental health problem occurring worldwide. Cognitive decline is frequently observed during acute and residual phases of MDD, contributing significantly to functional impairment. The aim of this study was to determine the clinical profile and correlates of cognitive decline amongst adult outpatients with MDD. Methods: The survey was cross-sectional in design. A systematic random sampling method was used to recruit patients. Confirmation of MDD was achieved by using the Mini International Neuropsychiatric Interview (M.I.N.I 7.0). Cognitive decline was measured using the Montreal Cognitive Assessment (MoCA). Descriptive analysis was performed, followed by univariate and multiple logistic regression analyses. Results: Out of 245 patients, 32.7% (n = 80, 95% CI: 26.7, 38.6) had cognitive decline. Multiple logistic regression showed the existence of cognitive decline amongst MDD patients, which was significantly associated with those having secondary and lower levels of education (OR: 6.09; 95% CI: 2.82, 13.16; p < 0.001), five or more depressive episodes (OR: 8.93; 95% CI: 3.24, 24.67; p < 0.001), treatment non-compliance (OR: 3.48; 95% CI: 1.40, 6.59; p = 0.003), and medical comorbidity (OR: 2.74; 95% CI: 1.46, 5.18; p = 0.002). Conclusions: Cognitive decline is a prevalent condition among outpatients with MDD. Clinicians need to be cognizant about measures of cognition and related risk factors. Timely control of both depression and medical comorbidities would be a reasonable approach to improve functional outcomes in MDD patients.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".