Neuropsychiatric Manifestations in Patients with Chronic Migraine: A Hospital-Based Study
Bibliographic record
Abstract
Background: Chronic migraine (CM) is a very disabling neurological disorder that goes beyond recurrent headache attacks, to include some important neuropsychiatric comorbidities. The depression, anxiety, sleep disturbance, and cognitive dysfunction are becoming recognized as innate characteristics of CM phenotype but there is a paucity of data on South Asian populations. Objectives: The aim of the study is to determine the prevalence and clinical correlation of neuropsychiatric manifestations in chronic migraine patients who reported to the tertiary care hospital in Pakistan. Methods: This observational cross-sectional study was conducted in the Department of Neurology, Mayo Hospital, Lahore, Pakistan, from March 2024 to June 2025. A total of 120 patients aged 18–60 years diagnosed with chronic migraine as per the International Classification of Headache Disorders-3 (ICHD-3) criteria were recruited through consecutive non-probability sampling. Neuropsychiatric assessments were performed using validated scales: the Patient Health Questionnaire-9 (PHQ-9) for depression, Generalized Anxiety Disorder-7 (GAD-7) for anxiety, Pittsburgh Sleep Quality Index (PSQI) for sleep quality, and Montreal Cognitive Assessment (MoCA) for cognitive function. Data were analyzed using SPSS v26.0, applying Chi-square tests for categorical variables and Pearson correlation analysis for inter-domain associations. A p-value <0.05 was considered statistically significant. Results: The age of 36.4 above and below was 9.7 that represented the mean age and females 68.3 that represented the percentage of the females. Depression was found in 46.7, anxiety in 41.6, and inappropriate sleep quality in 55.8 percent of patients, and 30.8 percent of patients had been found to be cognitively impaired. Depression and anxiety rates were much greater among females than among males (p<0.05). The longer the duration of migraine, the more likely that they impaired cognition (p=0.03). There was a significant relationship between sleep disturbance and depressive symptoms (r=0.54, p<0.001). Conclusion: The neuropsychiatric symptoms are very common in CM patients, especially in sleep disturbance, depression, and anxiety. These results underline the necessity to use integrated neuropsychiatric screening and multidisciplinary management to enhance the outcome of this susceptible group.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".