Correlations between alexithymia and pain severity, depression, and anxiety among patients with chronic and episodic migraine
Bibliographic record
Abstract
AIMS: Some studies have found elevated alexithymia among patients with chronic pain, but the correlations between alexithymia and the severity of pain, depression, and anxiety among migraine patients are unclear. The aims of the present study were to investigate whether individuals suffering from episodic migraine (EM) differ from those with chronic migraine (CM) in regards to depression, anxiety, and alexithymia measures and to investigate the association of alexithymia with the results of depression and anxiety test inventories and illness characteristics. METHODS: A total of 165 subjects with EM and 135 subjects with CM were studied. The Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), and Toronto Alexithymia Scale (TAS) were administered to all subjects. The correlation between alexithymia and sociodemographic variables, family history of migraine and illness characteristics (pain severity, frequency of episode, duration of illness) were evaluated. RESULTS: Compared with EM patients, the CM patients had significantly higher scores on measures of depression but not alexithymia and anxiety. There was a positive correlation between TAS scores and age and education in both migraine groups, but there was no correlation between TAS scores and other demographic variables. Depression and anxiety were significantly correlated with alexithymia in both migraine groups. CONCLUSION: Our results indicate that CM patients are considerably more depressive than EM patients. In this study, depression and anxiety were significantly correlated with alexithymia in both migraine groups. Our results demonstrate a positive association between depression, anxiety, and alexithymia in migraine patients.
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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 | 0.002 |
| 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) | 0.002 | 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".