Alexithymic and somatisation scores in patients with temporomandibular pain disorder correlate with deficits in facial emotion recognition
Bibliographic record
Abstract
Current studies suggest dysfunctional emotional processing as a key factor in the aetiology of temporomandibular disorder (TMD). Investigating facial emotion recognition (FER) may offer an elegant and reliable way to study emotional processing in patients with TMD. Twenty patients with TMD and the same number of age-, sex- and education-matched controls were measured with the Facially Expressed Emotion Labelling (FEEL) test, the 26-item Toronto Alexithymia Scale (TAS-26), the Screening for Somatoform Symptoms (SOMS-2a), the German Pain Questionnaire and the 21-item Hamilton Depression Rating Scale (HAMD). The patients had significantly lower Total FEEL Scores (P = 0·021) as compared to the controls, indicating a lower accuracy of FER. Furthermore, we were able to demonstrate significant group differences with respect to the following issues: patients were more alexithymic (P = 0·006), stated more somatoform symptoms (P < 0·004) and had higher depressive scores in the HAMD (P < 0·003). The factors alexithymia and somatisation could explain 31% (adjusted 27%) of the variance of the FEEL Scores in the sample. The estimation of the standardised regression coefficients suggests an equivalent influence of TAS-26 and SOMS-2a on the FEEL Scores, whereas 'group' (patients versus healthy controls) and depressive symptoms did not contribute significantly to the model. Our findings highlight FER deficits in patients with TMD, which are partially explained by concomitant alexithymia and somatisation. As suggested previously, impaired FER in patients with TMD may further point to probable aetiological proximities between TMD and somatoform disorders.
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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 | 0.000 |
| 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".