Olfactory function and apathy as potential biomarker in patients with Parkinson's disease
Bibliographic record
Abstract
Introduction: Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms as bradykinesia, rigidity, tremor and postural instability. Additionally, PD is usually associated with non-motor symptoms (NMS) that include smell and taste dysfunctions, neuropsychiatric symptoms such as apathy, anxiety and cognitive impairment, sleep problems and autonomic dysregulation [1-2]. \nThe aim of the study was first to investigate olfactory function, cognitive impairment, apathy and fatigue in PD patients in relation to healthy controls, and second to analyze the relationship between these NMS and the severity of motor symptoms in subjects with PD. \nMaterials and methods: One hundred and forty-seven participants were enrolled (96 PD patients, mean age in years: 67.5, SD: 7.2; 51 healthy controls; mean age: 65.1, SD: 11.8). Olfactory function was evaluated using the Sniffin’ Sticks test. The Montreal Cognitive Assessment (MoCA) was used to assess cognitive impairment. Apathy was examined by the Starkstein Apathy Scale (SAS) and fatigue was evaluated by the Parkinson’s Disease Fatigue Scale (PFS). \nResults: PD patients showed severe impairment in olfactory function compared to healthy controls. Moreover, in PD patients apathy and fatigue scores were significantly increased, while MOCA scores were significantly decreased in comparison to controls. Multivariate linear regression analyses showed that both apathy and UPDRS were associated with olfactory function. \nConclusion: Our results identified a greater level of apathy in PD patients affected by severe olfactory loss. Moreover, the present study confirms that alteration of olfactory parameters, such as odor threshold, identification, discrimination and TDI score, are related to other NMS. \nReferences: \n1) Poewe, 2008. Mov Disord 26(1):6–17. \n2) Scapira et al., 2017. Eur J Neurol 15(1):14–20.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".