Association of depressive symptoms with impaired episodic memory in patients with Parkinson's disease
Bibliographic record
Abstract
ABSTRACT Objective: To evaluate the correlation of depressive symptoms and impaired episodic memory in patients with Parkinson's disease (PD). Methods: This is a cross-sectional, non-probabilistic and intentional study. Individuals diagnosed with PD and aged 60 years or older, who were enrolled at two movement disorders outpatient clinics in the city of Maceió, AL, were selected. 62 elderly people were selected, divided into two groups, 40 with symptoms of depression and 22 without symptoms of depression. A sociodemographic questionnaire, Geriatric Depression Scale (GDS-15 reduced version), the Rey Auditory-Verbal Learning Test (RAVLT) and Montreal Cognitive Assessment (MoCA) were used. Results: In view of the sample of 62 elderly people, there was a high prevalence of female elderly, 64.5% with a mean age of 66.72 years (±5.12). Regarding sociodemographic data, a statistically significant difference was identified between groups only in the use of antidepressants (p < 0.001) and in relation to clinical characteristics, there was a difference in relation to episodic memory (RAVLT) (p < 0.001) of MoCA (p = 0.018) and in the abstraction (p = 0.044) and executive function (p = 0.021) domains of MoCA. Regarding the relationship between depressive symptoms and impaired episodic memory, a moderate inverse correlation was identified (r = −0.575; p < 0.001). Conclusion: PD associated with depressive symptoms presents impairments in episodic memory when compared to those without symptoms. In addition, it is possible to identify that the levels of depressive symptoms are directly proportional to the loss of episodic memory.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".