A cross-sectional study of insomnia severity and cognitive dysfunction in bipolar disorder and schizophrenia patients under remission
Bibliographic record
Abstract
Background: Sleep disturbances are commonly seen in mental illnesses such as schizophrenia, bipolar affective disorder, depression, anxiety, and substance use disorders. Even though psychiatric symptoms are treated, sleep disturbances remain to be persisting in some groups of patients. Persistent sleep disturbances could lead to relapse of the disorder per se or could lead to cognitive dysfunction or impairment. Depending on the severity of insomnia, cognitive impairment can vary among remitted patients. Methodology: A total of 200 patients suffering from mental illnesses such as schizophrenia and bipolar affective disorder under remission are taken for the study. After obtaining the sociodemographic profile of the patients, insomnia severity is calculated using the Insomnia Severity Index (ISI) scale and cognitive impairment is assessed using the Montreal Cognitive Assessment (MoCA). ISI scores are compared ith MoCA scores and cognitive impairment is assessed in those patients using statistical analysis. Results: The mean age as found to be 32.08, the mean ISI score is 20.55, and the mean MoCA score is 23.15. ISI score as negatively correlated to MoCA score and age. MoCA score as positively correlated to age. Conclusions: Cognitive impairment, as observed on MoCA score, as more hen the insomnia severity is high and also ith increasing age. Correcting the underlying insomnia in remitted patients is very important in preventing cognitive impairment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".