Facial recognition, laterality judgement, alexithymia and resulting central nervous system adaptations in chronic primary headache and facial pain—A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Patients with chronic headaches and chronic oro-facial pain commonly present psychosocial issues that can affect social interactions. A possible reason could be that patients with these disorders might present impairments in facial recognition, laterality judgement and also alexithymia. However, a systematic review summarizing the effects of facial emotion recognition, laterality judgement and alexithymia in individuals with headaches and oro-facial pain is still not available. AIM: The main objective of this systematic review (SR) and meta-analysis (MA) was to compile and synthesize the evidence on the occurrence of alexithymia, deficits in laterality or left-right (LR) recognition and/or facial emotion recognition (FER) in patients with chronic headache and facial pain. METHODS: Electronic searches were conducted in five databases (up to September 2023) and a manual search to identify relevant studies. The outcomes of interest were alexithymia scores, speed and accuracy in LR and/or FER, or any other quantitative data assessing body image distortions. The screening process, data extraction, risk of bias and data analysis were performed by two independent assessors following standards for systematic reviews. RESULTS: From 1395 manuscripts found, only 34 studies met the criteria. The overall quality/certainty of the evidence was very low. Although the results should be interpreted carefully, individuals with chronic headaches showed significantly higher levels of alexithymia when compared to healthy individuals. No conclusive results were found for the other variables of interest. CONCLUSION: Although the overall evidence from this review is very low, people with chronic primary headaches and oro-facial pain could be regularly screened for alexithymia to guarantee appropriate management.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".