Alexithymia and facial emotion recognition in patients with craniofacial pain and association of alexithymia with anxiety and depression: a systematic review with meta-analysis
Bibliographic record
Abstract
Background We aimed to determine the presence of alexithymia in patients with craniofacial pain (CFP) compared with asymptomatic individuals. Our secondary aims were to assess the relationship of alexithymia with anxiety and depression levels, as well as to assess the presence of facial emotion recognition deficit. Methods Medline, Scielo and Google Scholar were searched, with the last search performed in 8 September 2021. Standardized mean differences (SMDs) and 95% CIs were calculated for relevant outcomes and were pooled in a meta-analysis using the random effects model. In addition, meta-analyses of correlations and a meta-regression of alexithymia with depression and anxiety were performed. Results Regarding alexithymia, assessed through the Toronto Alexithymia Scale (TAS), the results showed significant differences, with higher values in patients compared with asymptomatic individuals, with a large clinical effect (SMD 0.46; 95% CI [0.22–0.71]; heterogeneity-Q 66.86; p < 0.001; inconsistency (I 2 ) = 81%). We found statistically significant correlations with a small clinical effect of alexithymia with anxiety and depression. The meta-regression showed no significant association between the TAS and anxiety or depression. With respect to facial emotion recognition, the results showed statistically significant differences, with greater recognition difficulty in patients compared with asymptomatic individuals, with a large clinical effect (SMD −1.17; 95% CI [−2.01 to −0.33]; heterogeneity-Q 2.97; p = 0.080; I 2 = 66%). Conclusions Patients with CFP showed alexithymia with moderate evidence. There was also moderate evidence indicating that these patients had significant deficits in facial emotion recognition compared with asymptomatic individuals. Furthermore, alexithymia showed statistically significant correlations with anxiety and depression levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".