Alexithymia Increases the Headache Pain Index in Women with Migraine: Preliminary Results
Bibliographic record
Abstract
Background: Alexithymia is characterized by a deficit in identifying and communicating feelings. Emerging evidence suggests that it is highly prevalent in migraine, where it could affect the pain expression. This pilot study on female migraineurs aimed at assessing any relationship between alexithymia and headache attacks in terms of frequency and pain intensity. Methods: All the patients (42) who fulfilled the diagnostic criteria for migraine were enrolled in this pilot, observational, cross-sectional study after having obtained written informed consent. A psychological assessment was made of each patient to identify any alexithymia using the TAS-20 scale, for anxiety/mood comorbidity (the STAI-Y1, STAI-Y2, and BDI-II) and for migraine-related disability (the HIT-6). An HPI index (attack frequency x pain intensity) was also calculated for each patient, based on their headache diaries. A multivariate analysis was performed to investigate any association among the TAS-20 score, HPI score, and the following covariates: BDI-II, STAI-Y1, STAI-Y2, HIT-6 scores, age, education, and disease duration. Results: Overall, 35.6% of the sample were given a diagnosis of alexithymia. After removing a subgroup of 7 subjects with HPI > 100, with more severe psychiatric comorbidity and a longer disease duration from the whole sample, a multivariate analysis detected a statistically significant (p = 0.010) association between the HPI and TAS-20 scores. Conclusions: This pilot study suggests that alexithymia may play a role in increasing the frequency and pain intensity of migraine attacks, consequently worsening disability in female migraineurs. Further studies are required to confirm this finding.
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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 | 0.000 | 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) | 0.003 | 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".