Alexithymia, assertiveness and headache impact
Bibliographic record
Abstract
Introduction High levels of alexithymia as well as low scores on assertiveness have been described in patients with chronic pain and headache. Objectives To determine alexithymia and assertiveness scores and to explore their association with headache impact, in primary chronic headache patients. Aims This study aims to advance knowledge of the emotional expressiveness in headache impact. Methods In a sample of 62 outpatients, we used the Toronto Alexithymia Scale (TAS-20), the Rathus Assertiveness Scale and the Headache Impact Test (HIT-6) and applied the Pearson correlation index. Results 77.4% of women, 36.3 years mean age. The most prevalent diagnoses are migraine combined with tension type headache (33.9%), migraine alone (32.3%) and tension-type headache alone (22.6%). Most of the patients have not any psychiatric comorbidity (77.8%). We observe a direct linear relationship and statistically significant difference, between the total impact of headache and the total score of alexithymia (r = 0.27 p = 0.03) and there is an inverse correlation between the impact of headache and the total score of the scale of assertiveness, not statistically significant (r = −0.004 p = 0.97). Discriminated by diagnostic groups, we found that the association between assertiveness and headache impact remains only in patients with migraine alone, while that between alexithymia and headache impact is preserved in all subgroups. Conclusion Two indirect measures of the difficulties in emotional expressiveness such as alexithymia and assertiveness, show the expected association with headache impact. The sample size can influence some of the correlations not statistically significant.
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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 | 0.001 |
| 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".