[Characteristics of cognitive impairment in depressive elders in Shanghai community].
Bibliographic record
Abstract
OBJECTIVE: To explore the characteristics of cognitive impairment in depressive elders in Shanghai. METHODS: A total of 1 068 participants randomly selected from 4 communities in Shanghai underwent neuropsychiatric and psychiatrists clinical assessment. Among them, 102 depressive ones with a score of geriatric depression scale (GDS) > 10 and 102 non-depressive ones with a score of GDS ≤ 10 were selected as depression and non-depression groups respectively. The SPSS statistic software V17.0 was used. RESULTS: Significant differences existed between the depressive and non-depressive patients in the total score of Montreal Cognitive Assessment (MoCA) (t = 2.353), trail making B task (t = 2.236), attention (t = 2.621), sustained attention (t = 2.381), calculation (t = 2.612) and fixed orientation (t = 2.259) (P < 0.05). The negative correlation had significant inter-group differences in the total score of MoCA (r = -0.142), attention (r = -0.161), sustained attention (r = -0.160), calculation (r = -0.150), fluency (r = -0.156), delayed recall (r = -0.175) and orientation to place (r = -0.172) (P < 0.05). Cognitive impairment in depression group had lower scores than non-depression group in the total score MoCA (14.9 ± 7.3 vs 17.5 ± 6.7), attention (1.1 ± 0.8 vs 1.4 ± 0.8), calculation (1.9 ± 1.2 vs 2.3 ± 1.1) and fixed orientation (1.7 ± 0.7 vs 1.8 ± 0.5) (P < 0.05). CONCLUSION: In depressive elders, cognitive impairment occurs in multiple cognitive domains of attention, executive function and orientation to place. There is a negative correlation between GDS score and MoCA. The higher GDS score, the worse cognitive function.
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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 | 0.001 |
| 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".