Malnutrition and Associated Motor and Non-motor Factors in People with Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: People with Parkinson's disease (PwP) are at higher risk of developing malnutrition. Several factors have been suggested to be involved including motor symptoms, non-motor symptoms, and treatment-related complications. OBJECTIVE: The objective of the study was to analyze the combined effect of motor, non-motor, and pharmacological factors in the risk of malnutrition in PwP. METHODS: Eighty-seven consecutive PwP were included in the study. Clinical data and pharmacological treatment were collected. Nutritional status was assessed using the Mini-Nutritional Assessment (MNA) questionnaire. Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Non-motor Symptoms Scale (NMSS), Hamilton Depression Rating Scale HAM-D, and Montreal Cognitive Assessment were applied. RESULTS: Thirty (34.4%) PwP were at risk of malnutrition and seven had malnutrition (8%). Abnormal nutritional status was associated with lower education, higher MDSUPDRS Parts I, II, and III and total scores, and higher scores in the NMSS domain of sleep disorders and fatigue. MDS-UPDRS motor score remained as a determinant of abnormal nutritional status, defined as MNA < 23.5, with an odds ratio 1.1 (95% confidence interval 1.01-1.10, p = 0.02). CONCLUSION: The main factor associated with nutritional status was severity of the motor symptoms as assessed by the MDS-UPDRS Part III. Non-motor symptoms and treatment-related complications were not associated with malnutrition.
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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 | 0.002 |
| 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) | 0.001 | 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".