Large-Fiber Neuropathy in Parkinson’s Disease: Clinical, Biological, and Electroneurographic Assessment of a Romanian Cohort
Bibliographic record
Abstract
(1) Background: Increased attention has lately been given to polyneuropathy in Parkinson’s Disease (PD). Several papers postulated that large-fiber neuropathy (PNP) in PD is related to vitamin B12 deficiency and L-Dopa exposure. (2) Methods: Using a cross-sectional, observational study, we evaluated 73 PD patients without a previously known cause of PNP using clinical scores (UPDRS II and III and Toronto Clinical Scoring System), biological evaluation of vitamin B12 and folic acid, and nerve conduction studies to assess the prevalence and features of PNP. (3) Results: The prevalence of PNP was 49.3% in the study group. In the L-Dopa group, the frequency of PNP was 67.3% as compared to PNP in the non-L-Dopa group, where one subject had PNP (χ2 = 23.41, p < 0.01). PNP was predominantly sensory with mild to moderate axonal loss. Cyanocobalamin correlated with L-Dopa daily dose (r = −0.287, p < 0.05) and L-Dopa duration of administration (r = −0.316, p < 0.05). L-Dopa daily dose correlated with the amplitudes of sensory nerve action potentials of the superficial peroneal and radial nerves (r = −0.312, p < 0.05) (r = −0.336, p < 0.05), respectively. (4) Conclusions: PNP is more frequent in L-Dopa-treated patients than in L-Dopa-naïve patients. The results imply that longer exposure to high doses of L-Dopa may cause vitamin B12 and folate imbalance and PNP, secondarily.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".