Dysosmia Is a Predictor of Motor Function and Quality of Life in Patients with Parkinson’s Disease
Bibliographic record
Abstract
(1) Background: The correlation between dysosmia with quality of life (QoL) in patients with PD was rarely reported. The study aimed to examine the effect of dysosmia on motor function and QoL in PD. (2) Methods: This cross-sectional study, performed between October 2016 and February 2021, recorded the traditional Chinese version of the University of Pennsylvania Smell Identification Test (UPSIT), the Montreal Cognitive Assessment (MoCA), the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS UPDRS), and the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in patients with PD. UPSIT = 19 was applied to separate the total anosmia and non-anosmia groups. (3) Results: 243 patients with PD were recruited. The total anosmia group had higher MDS UPDRS total, part II, and part III scores than the non-anosmia group. They also had worse scores on the dimensions of activities of daily living (ADL) and cognition of the PDQ-39 than the non-anosmia group. The UPSIT score correlated MDS UPDRS part III score (p < 0.0001), PDQ-39 ADL quartile (p = 0.0202), and Dopamine transporter scan (p = 0.0082) in the linear regression. (4) Conclusions: Dysosmia in PD predicted a phenotype with defective motor function, ADL, and cognition QoL. The findings supported the olfactory transmission of α-synuclein to the cortices, substantia nigra.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".