The Association of Olfactory Dysfunction With Depression, Cognition, and Disease Severity in Parkinson's Disease
Bibliographic record
Abstract
Background: Non-motor subtypes of Parkinson's disease (PD) include the limbic, cognitive, and brainstem phenotype, which may have different pathological pathways with olfaction. In this work, we aim to clarify the association between olfactory dysfunction, depression, cognition, and disease severity in PD. Methods: A total of 105 PD subjects were included and divided into anosmia and non-anosmic groups, using the University of Pennsylvania Smell Identification Test (UPSIT). All patients were evaluated with the movement disorder society unified Parkinson's disease rating scale (MDS-UPDRS), the Beck depression inventory (BDI)-II, and the Montreal cognitive assessment (MoCA). Results: The BDI-II and UPSIT scores had a trend of reverse correlation without statistical significance (β-coefficient −0.12, p = 0.232). However, the odds ratio (OR) in anosmia was 2.74 (95% CI 1.01–7.46) for depression and 2.58 (95% CI 1.06–6.29) for cognitive impairment. For the MDS-UPDRS total and Part 3 score, the anosmia had a β-coefficient of 12.26 (95% CI 5.69–18.82) and 8.07 (95% CI 3.46–12.67), respectively. Neither depression nor cognitive impairment is associated with motor symptoms. Conclusion: More severe olfactory dysfunction in PD is associated with cognitive impairment and greater disease severity. Depression in PD may involve complex pathways, causing relatively weak association with olfactory dysfunction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".