Exploring Facial Somatosensory Distortion in Chronic Migraine: The Role of Laterality and Emotion Recognition—A Cross-Sectional Study
Bibliographic record
Abstract
Background: In addition to being highly distressing, chronic migraine headaches are a major socioeconomic challenge. Like other pain syndromes, migraine headaches are associated with psychological and physiological impairments, including sensorimotor and somatosensory deficits. Aim: This study aims to explore whether patients with high-frequency or chronic migraine differ from a healthy population in the areas of laterality recognition (LAT) and facial emotion recognition (FER) and whether there are correlations between these areas and central sensitization of pain and psychological components like stress, depression, anxiety, and alexithymia. Methods: Using a cross-sectional design, individuals with high-frequency or chronic (ICHD classification) migraine (migraine group MG = 45) and healthy individuals (control group CG = 25) were studied using LAT testing (hand, neck, and face); FER testing; and questionnaires, including the Central Sensitization Inventory (CSI) and the Toronto Alexithymia Scale (TAS-20). Results: Data from 70 participants were collected for analysis. Statistically significant differences were found only in the assessment of central sensitization (p < 0.001). Weak to moderate monotonic correlations were found for the MG, especially between alexithymia detection (TAS-20) and facial emotion recognition (FER test). Discussion: The methodological procedure and its accompanying challenges can be seen as limitations of this study. The lack of significant effects must be mentioned, but the selection of the collected questionnaires, the uniform diagnostics, and the statistical processing of a large amount of data represent methodological strengths. Conclusion: The CSI and the TAS-20 could be used in combination with FER to assess chronic migraine. Implementing the described sensorimotor parameters as a therapeutic intervention requires further investigation.
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.
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 | 0.001 | 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) | 0.001 | 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".