Cognitive Deficits in Patients of Depressive Disorder
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of cognitive deficits in patients of Depressive disorder. METHODOLOGY: This cross-sectional study was conducted at Jinnah Post Graduate Medical Centre (JPMC), Karachi,from September 2018 to March 2019. The sample size of 250 was calculated through customary techniques, and the sampling technique was non-probability consecutive sampling. Those patients who were diagnosed with cases of depressive disorder were enrolled in the study. The data were analyzed using SPSS (Statistical Packages of Social Sciences) version 22.0. RESULTS: Out of the total of 250 cases, 114 (45.60%) were males, and 136 (54.40%) were females with an average age of 33.6211.07 years. Among 250, the majority, 178 (71.20%), were married and Illiterate 90 (36.00%). Among all participants, 136 (54.4%) belonged to middle socio-economic and 120 (68.0%) were household by occupation. Out of 250 cases, 135 (54%) were drug nave, while 114 (45.6%) were on active treatment. Cognitive dysfunction was present among 169 (67.6%). Educational status, treatment status, and duration of diseases were considerably related, with cognitive dysfunction having a p-value of less than 0.05. CONCLUSION: The rate of Cognitive dysfunctions among patients with depressive disorder is high and alarming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".