The relationship between alexithymia, sensory phenotype and neurophysiological parameters in patients with chronic upper limb neuropathy
Bibliographic record
Abstract
In this study, we investigated the relationship between sensory abnormalities evaluated by quantitative sensory testing (QST) and alexithymia, depression and anxiety in patients with neuropathic pain involving the upper limbs. We enrolled 62 patients (34 with carpal tunnel syndrome, 7 with brachial plexopathy, 3 with cervical painful radiculopathy, 5 with ulnar entrapment neuropathy at elbow and 13 with post-burn hypertrophic scars) and 48 healthy controls. All underwent nerve conduction studies (NCS), evaluation of cold, heat pain and vibration detection threshold (VDT) by QST and evaluation of alexithymia by Toronto Alexithymia Scale (TAS-20), depression by Beck Depression Inventory II (BDI-II), anxiety by State-Trait Anxiety Inventory (STAI-Y), level of psychological distress by 12-item General Health Questionnaire (GHQ-12) and perceived social support by the Multidimensional Scale of Perceived Social Support (MSPSS). The general linear model analysis revealed a significant relationship between TAS-20 overall and TAS-20 sub-score for difficulty identifying feelings and VDT z-scores in the left index with no interaction by year of education and sensory NCS results. Our results demonstrated the association between impairment of vibratory sensation of the left hand, reflecting cutaneous mechanoceptor dysfunction, and alexithymia, particularly the difficulty to identify feelings. The importance of delivering to patients with neuropathic pain personalized care that takes into account not only the neurophysiological aspects but also the aspects of mental functioning is discussed.
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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 | 0.001 | 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) | 0.001 | 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".