Clinical symptoms and their relationship with cognitive impairment in elderly patients with depressive disorder
Bibliographic record
Abstract
Objective To evaluate the correlation between clinical symptoms and cognitive impairment in elderly patients with depressive disorder. Methods In this retrospective study, a total of 123 elderly patients with depressive disorder admitted to our hospital from January 2020 to February 2021 were included. Patients' cognitive function was assessed by the Montreal Cognitive Assessment Scale (MoCA). According to the combination of cognitive impairment or not, patients were divided into the combined group (64 cases) and the depressive disorder group (59 cases). In addition, 70 healthy people who came to our hospital for physical examination during the same period were randomly selected as the healthy group. Results The incidence of severe cognitive impairment in the combined group (33, 51.56%) was significantly higher than that in the depression group (19, 32.20%), the difference was statistically significant (P = 0.003). The incidence of somatization symptoms, suicidal tendency, retardation of thinking, diminution of energy, anxiety and sleep disorder in the combined group were higher than that in the depressive disorder group with significant difference [30 (56.88%) vs. 16 (27.12%), P = 0.024; 12 (18.75%) vs. 3 (5.08%), P = 0.021; 33 (51.56%) vs. 14 (23.73%), P = 0.002; 37 (57.81%) vs. 23 (38.98%), P = 0.029; 42 (65.63) vs. 25 (42.37), P = 0.011; 50 (78.13) vs. 42 (71.19), P = 0.031, respectively]. Spearman rank correlation analysis suggested that somatic symptom, mood change, suicidal tendency, retardation of thinking, diminution of energy, anxiety, and sleep disorder were negatively correlated with cognitive impairment, respectively (r =-0.161, −0.672, −0.262, −0.871, −0.421, −0.571, −0.512, P < 0.001). Conclusion The clinical symptoms of depressive disorder were negatively correlated with cognitive impairment. Somatic symptoms, suicidal tendency, retardation of thinking, diminution of energy, anxiety, and sleep disorder were the risk factors for cognitive impairment.
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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 | 0.000 |
| 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".