The impact of associated large-fiber peripheral neuropathy on health-related quality of life in Parkinson’s disease – results from a Romanian cohort
Bibliographic record
Abstract
The impact of associated large-fiber peripheral neuropathy on health-related quality of life in Parkinson's disease -results from a Romanian cohort AbstrAct background.Recent studies described a higher prevalence of peripheral neuropathy (PN) in Parkinson's disease that was linked to L-Dopa exposure.Peripheral neuropathies are known causes of a decreased health-related quality of life (HrQoL).Until now, no studies addressed the issue of how or if associated PN in PD affects HrQoL.Methods.In a cross-sectional, observational study, 73 non-demented PD patients, from which 36 with confirmed PN based on clinical (using the Toronto Clinical Neuropathy Scale-TCSS) and nerve conduction studies completed the Romanian version of PDQ-39.results.Significant differences between mean scores in Motor (49.86 (27.61) vs. 31.50(26.24), p = 0.005), Activities of daily living (49.86 (27.61) vs. 31.75(30.10), p = 0.003) and body discomfort (52.54 (29.54) vs. 23.64 (18.48), p = 0.002) domains of PDQ-39 in the PN-PD group versus non-PN group were observed.TCSS significantly correlated to motor, emotional well-being and body discomfort domains (r = 0.406 p< 0.001; r = 0.316 p = 0.007; r = 0.356 p = 0.002, respectively).The multivariate linear regression model showed that motor impairment and PN correlated to motor domain (beta = 0.601, p = 0.000; beta = 0.211 p = 0.041, respectively) and PN significantly correlated to body discomfort domain (beta = 0.314, p = 0.020) of PDQ-39.conclusions.The presence of associated PN in PD determines a further deterioration of HrQol in subjects with already a poorer HrQol.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".