Emotion Processing in Peripheral Neuropathic Pain: An Observational Study
Bibliographic record
Abstract
BACKGROUND: In clinical practice, the implementation of tailored treatment is crucial for assessing the patient's emotional processing profile. Here, we investigate all three levels of analysis characterizing emotion processing, i.e., recognition, representation, and regulation, in patients with peripheral neuropathic pain (PNP). METHODS: Sixty-two patients and forty-eight healthy controls underwent quantitative sensory testing, i.e., psychophysical tests to assess somatosensory functions such as perception of cold (CDT), heat-induced pain (HPT), and vibration (VDT), as well as three standardized tasks to assess emotional processing: (1) the Ekman 60-Faces Test (EK-60F) to assess recognition of basic facial emotions, (2) the Reading the Mind in the Eyes Test (RME) to assess the ability to represent the feelings of another person by observing their eyes, and (3) the 20-item Toronto Alexithymia Scale (TAS-20) to assess emotional dysregulation, i.e., alexithymia. RESULTS: General Linear Model analysis revealed a significant relationship between left index finger VDT z-scores in PNP patients with alexithymia. The RME correlated with VDT z-scores of the left little finger and overall score for the EK-60F. CONCLUSIONS: In patients with PNP, emotion processing is impaired, which emphasizes the importance of assessing these abilities appropriately in these patients. In this way, clinicians can tailor treatment to the needs of individual patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".