Cognitive Impairment and Its Correlation with Depression
Bibliographic record
Abstract
Objective: To determine the correlation between the severity of depression and cognitive impairment. Study Design: Cross-sectional study. Place and Duration of Study: Department of Psychiatry, Combined Military Hospital, Gujranwala, Pakistan from May 2016 to December 2016. Methodology: The cross-sectional study was conducted on outpatients in the Department of Psychiatry at Combined Military Hospital Gujranwala. The diagnosis of depression was made based on the WHO's ICD10 diagnostic criteria, and symptom severity was assessed using the Beck Depressive Inventory. Deirdre M. used the Montreal Cognitive Assessment version 7.1 to assess cognitive impairment. Results: Eighty-six subjects were included in this study. A comparison of cognitive impairment and depression revealed that in a total of 16 subjects with minimal depression, only 5 had cognitive impairment; in 14 subjects with mild depression, 11 showed cognitive impairment; 26 subjects had moderate depression, out of which 18 showed signs of cognitive impairment; and among 30 subjects with severe depression, there was cognitive impairment in 25 individuals. The Spearman correlation showed a weak correlation of 0.321 (p<0.001). Conclusion: A high level of depressive symptoms, although weak, is significantly correlated with cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.002 | 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".