Correlations between alexithymia and pain severity, depression, and anxiety among patients with chronic and episodic migraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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 it