Relationship of depressive disorders and cognitive function impairment for the elderly
Bibliographic record
Abstract
Objective: To explore possible effects of depressive disorder on cognitive function for the elderly,and investigate relationship of them.Methods: Montreal cognitive assessment(MoCA) and simplified version of the SCID depression disorders questionnaire were administered to 3903 participants from 24 villages(neighborhood) committees in Pudong new area.Results: There were 519 cases with depressive disorder,wherein the most cases suffered from dysthymia(54.91%) and the least cases had major depression(5.4%).There were significant differences in factor scores like MoCA total score,memory,attention,verbal fluency,abstract ability,and delayed recall between elderly depressive disorder group and negative group(P 0.05).Logistic regression analysis showed that gender,occupation,life ability,diabetes and hyperlipoidemia were risk factors for elderly depressive disorder.Conclusions: The cognitive function impairment of the elderly with depressive disorder is broad and is positively correlated with the depsression severity.An active treatment can effectively improve the cognitive fuction levels.
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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 | 0.001 |
| 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".