Subjective Cognitive Impairment in Major Depressive Disorder: Predictors and Clinical Outcomes
Bibliographic record
Abstract
Aim: Individuals with subjective cognitive impairment (SCI) and major depressive disorder (MDD) are at high risk of developing neurodegenerative diseases. Studies suggest that SCI may be associated with concurrent cognitive performance and is also strongly associated with depressive symptoms. In this context, we aimed to evaluate the relationship between SCI and depression severity and to reveal predictive factors for SCI with current cognitive performance. Methods: Forty-two patients with MDD were divided into two groups: patients with and without SCI. All participants were administered the Montreal Cognitive Assessment (MoCA) and the Hamilton Depression Rating Scale (HAM-D). A logistic regression model was used to add demographic characteristics to the results obtained. Results: Individuals with SCI (n=21) were older (p=0.014) and less educated (p=0.006) than non-SCI patients (n=21) with MDD. Additionally, the mean of the MoCA total score was significantly higher in the non-SCI group (Student's t Test, p=0.011). Depression severity was the same throughout the groups (HAM-D, 𝜒2: 2.10, p= 0.28). Language dysfunction is one of the MoCA subtests, language impairment emerged as a key predictor in the binary logistic regression model, identified through Backward Wald elimination, for detecting depressive patients with SCI (p=0.007; OR=0.366; 95% CI 0.177-0.756). Conclusion: Our findings suggest that SCI accurately reflects concurrent cognitive performance in a clinic-based sample of MDD. The results also suggest that the clinical interpretation of SCI should take into account the possible impact of depression on the language domain.
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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 | 0.000 |
| 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 | 0.000 | 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".