Recognition of Emotional Facial Expressions and Alexithymia in Patients with Chronic Facial Pain
Bibliographic record
Abstract
Objectives Alexithymia, conceived as difficulties to identify emotions, is said to be related with several pain syndromes. This study examined the recognition of facially expressed emotions and its relation to alexithymia in subjects with chronic facial pain. Methods A total of 62 subjects were recruited, with n=20 patients with chronic facial pain and n=42 healthy controls. All subjects were tested for the recognition of facially expressed emotions (Facially Expressed Emotion Labelling Test (FEEL test). The Toronto Alexithymia Scale (TAS-26) was used for the diagnosis of alexithymia. Results Patients with chronic facial pain performed worse than controls at the FEEL task (p<.001) and showed higher total TAS scores (p<.001). This indicates the presence of alexithymia and facial emotion recognition deficits in the facial pain group. Discussion It was concluded from the results that both the recognition of facially expressed emotions, and the ability to identify and describe one’s own feelings (TAS), are restricted in chronic orofacial pain patients. This relationship is particularly important in the treatment of chronic facial pain, indicating that it should become part of the treatment in addition to the therapeutic key issues, to influence the quality of life of the affected patients positively.
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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 | 0.000 | 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) | 0.002 | 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".