Cognitive function characteristics and influencing factors in patients with depressive disorders: a 5-year follow-up report based on the epidemiological survey of mental disorders in Shandong Province, China
Bibliographic record
Abstract
Objective: Cognitive impairment represents a notable feature of depressive disorders. Comprehending its characteristics and influencing factors is vital for patient rehabilitation. Methods: This study is based on the 2015 Shandong Province epidemiological survey of mental disorders, from which 871 individuals meeting DSM-IV criteria for depressive disorders were selected as the research group. Using 1:1:1 matching by sex, age, and residence, we randomly selected 825 individuals with no DSM-IV diagnosis but positive on GHQ-12 and additional risk assessments as the elevated risk control group, and 825 with negative screenings as the minimal risk control group. In 2020, a follow-up survey was conducted, resulting in a final analysis of 1,855 cases. The survey included demographic data, various clinical information, and a series of screening and questionnaire assessments. Results: <0.01). The Analysis indicated that males, with better life quality, poor early psychological status and, and longer education were associated with higher MoCA scores. Conversely, older age, rural residency, employment, current depression, and poor marital were associated with lower MoCA scores. Conclusion: Individuals with depression commonly suffer cognitive impairment, which tends to partially improve as depression remits. Individual cognitive function is influenced by early psychological health, depressive status, quality of life, age, sex, educational level, residence, occupational, and marital status.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".