Analysis of Cognitive Functions in Population Having Major Depressive Disorders
Bibliographic record
Abstract
Background Major Depressive Disorder (MDD) is a complex mental health disorder that has an impact on many facets of cognitive performance. MDD's cognitive symptoms might lead to overall impairments in daily functioning and quality of life. It's vital to remember that the severity and appearance of these cognitive symptoms can differ from person to person. The aim of this study is to assess the cognitive function (attention, learning, memory, decision making and executive functions) in major depressive disorder individuals. MethodsThe study was conducted in the Department of Physiology, RUHS College of Medical Sciences and associated hospitals on 90 subjects having major depressive disorder of either sex in the age group 20-40 years. Cognitive function parameters (Mini mental status examination, Montreal cognitive protocol A & B, P300 latency, and amplitude) were assessed and data were presented as mean, standard deviation (SD) and correlation coefficient was found using Pearson correlation, and p-value<0.05 considered as statistically significant. ResultsThe mean score of cognitive parameters was increased which was shown as decrease in score of Montreal cognitive assessment (11.22±2.73), mini mental status examination (10.19±1.56). Delay in P300 latency (401.38±11.30) was also seen with increase in Hamilton D (18.33±6.7) score with correlation coefficient r= 0.758. Conclusions A strong positive correlation was observed between the Hamilton Depression (HAM-D) score and cognitive function parameter (P300), indicating that greater severity of depression is associated with a decline in cognitive performance. The relationship between depression and cognitive function is complex and varies among individuals.
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 | 0.000 | 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".