The Detection of Peripheral Neuropathy by Clinical Scales in Patients Diagnosed With Alcohol Use Disorder
Bibliographic record
Abstract
Introduction Peripheral neuropathy is a well-known manifestation of alcohol overconsumption, but neurophysiological confirmation of peripheral nerve damage is costly and sometimes involves invasive procedures. The aim of this study was to investigate the ability of commonly used clinical scales to detect the presence of neuropathy in patients with alcohol use disorder (AUD). Methods Data were collected retrospectively on 116 patients diagnosed with AUD and treated voluntarily in a detoxification special unit. Ninety-eight age and gender-matched healthy subjects without a diagnosis of AUD were used as the control group. The five tested clinical neuropathy scales were the Neuropathy Symptoms Score (NSS), the Neuropathy Disability Score, the Neuropathy Impairment Score (NIS), the Neuropathy Impairment Score of the Lower Limbs, and the modified Toronto Clinical Neuropathy Scale. Results The mean values of all tested clinical scales of the patients with AUD were significantly higher than the control group. All examined clinical scales were determined to be useful in discriminating between patients with neuropathy from patients without neuropathy. The strongest discrimination was seen with the NIS, including the best sensitivity and specificity for the range of scores obtained. All scales, except NSS, showed a stronger correlation with measures of large (LFN) than small fiber neuropathy (SFN). Conclusion Our study suggests that clinical scales could be used to detect peripheral neuropathy in patients with AUD when neurophysiological testing is not available. Moreover, we suggest that the NIS and the NSS scales would be most helpful in assessing LFN and SFN, respectively, in patients with AUD.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".