Study of cognitive impairment in depression
Bibliographic record
Abstract
Introduction Cognitive impairment is frequently observed in patients suffering from depression. Cognitive dysfunction play a critical role in increasing the individual’s vulnerability for the first onset, maintenance and future recurrence of depressive episodes. Objectives The objectiv was to assess the cognitive impairment in patient with depressive episode. Methods A cross sectional, hospital based study was conducted among 100 patients with depressive episodes diagnosed by International Classification of Diseases - 10 visiting outpatient and inpatient in Department of Psychiatry of Manipal Teaching Hospital, Pokhara, Nepal. The subjects were interviewed with Beck’s depression inventory, Perceived deficient questionnaire, Frontal assessment battery, Trail making test A and B and Forward and Backward Digit Span test. For the assessment of correlates, regression analyses were done using SPSS v 20.0. Results The mean age of the participants was 32.47 years (SD±12.25), majority were female, married, Hindu and from urban population. Higher number of respondent were student. Most of them were educated till intermediate level and belonged to middle socioeconomic class family. Different domain of cognitive function according to severity of depression was found to be statistically significant (p<0.05). This study also found that age, sex, education, medication use and Becks depression inventory score predicted the cognitive function. Conclusions Cognitive impairment is not ucommon among patient with depressive episodes. The impairment is not only seen in severe cases but also in mild to moderate cases. The assessment of cognitive deficits should be the regular part of the assessment in depressive patients.
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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 | 0.000 |
| 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".