STUDY OF INTER-RELATIONSHIP BETWEEN QUALITY OF LIFE AND COGNITION IN PEOPLE WITH EPILEPSY CROSS SECTIONAL STUDY FROM NORTH COASTAL ANDHRA PRADESH
Bibliographic record
Abstract
Objective: To study the Inter – relationship between Quality of life and Cognitive dysfunction in People with Epilepsy and to identify the factors that influence Cognition and QOL in PWE. Methods: We analyzed the factors that were independently associated with QOLIE-31, MMSE and MOCA scores which included demographic and clinical variables using Chi square, ANOVA and Multivariate regression analysis. Pearson coefficient calculator to know the interrelationship between QOLIE-3 scores, MMSE and MOCA. Results: We found a significant association between polytherapy, TLE and LRE with QOLIE-31 scores (p value being 0.0007 and <0.00001 respectively). We found a significant association between low MMSE scores and long duration of epilepsy more than 6 years( p: 0.001 and 0.002), statistically highly significant association when compared with TLE and LRE (p: 0.000). However MOCA showed strong positive correlation with QOLIE-31 scores when compared with MMSE. We found a moderate positive correlation with r value being 0.6 and a significant p value being <0.0001. Correlation between MOCA and QOLIE 31 score showed a significant positive correlation with an r value of 0.7 and a P value of <0.0000. Correlation between total MMSE and MOCA scores showed a significant positive correlation of r value being 0.8, p value being 0.000. Conclusion: Polytherapy, Long duration of Epilepsy, Temporal Lobe and other Focal Epilepsies, Poor Quality of Life standards are all independent factors determining the Cognitive dysfunction. There seems to be bidirectional relationship between Quality of Life and Cognitive dysfunction. MOCA seems to be superior to MMSE for Neurocognitive screening in PWE.
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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".