Impairment of facial emotion recognition in temporomandibular disorder
Bibliographic record
Abstract
Background: The aim of this study was to investigate emotional processing in temporomandibular disorder (TMD) reflected in facial emotion recognition (FER). As deficits in FER were suggested previously as general feature of somatoform disorders (SFD) [1], we searched for analogies between TMD and SFD trying to shed light on the etiology of TMD. Methods: Twenty patients with TMD and the same number of age, sex and education matched healthy controls were recruited to be measured with the Facially Expressed Emotion Labelling (FEEL) Test of FER, the 26-item Toronto Alexithymia Scale (TAS-26), the 21-item Hamilton Depression Ration Scale (HAMD) and the German Pain Questionnaire (the latter only for patients). Results: Patients had a significant lower Total-FEEL-Score (p = 0.021) compared to the controls, rated themselves significantly more alexithymic in the TAS-26 (p = 0.003) and were rated significantly more depressive in the HAMD (p > 0.001). However in the correlation analyses with FER only the association with pain related complaints showed a significant result (p = 0.03). Conclusion: Impaired FER detected in patients with TMD may give hints of possible etiologic proximities of TMD to SFD having in mind a common deficit in central emotional processing reflected in a strong divergence between subjective complaints and objective pathology [2]. References: [1] Pedrosa Gil F et al, Depress Anxiety 2009; 26: 26–33. [2] Ohrbach R et al, Pain 1998; 74: 315–26
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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".