Influence of age, schooling and cognition on olfactory test performance in Parkinson's Disease patients: a case-control study
Bibliographic record
Abstract
Background: olfactory dysfunction is an early and prevalent non-motor symptom of Parkinson’s disease (PD). However, factors such as age, schooling, and cognition also influence olfactory test performance and are essential for the proper interpretation of results, especially in populations with low educational levels. Objectives: to evaluate the influence of age, schooling, and cognition on olfactory test performance in Parkinson’s disease patients (PDG) and the control group (CG). Materials and Methods: this cross-sectional case-control study included 106 participants (53 PDG and 53 CG), aged 60 to 85. All underwent olfactory testing with Sniffin´Sticks-12 (SS-12) and the modified Connecticut Chemosensory Clinical Research Center (mCCCRC) and cognitive screening with the Montreal Cognitive Assessment (MoCA). The PDG was scored by part III of the UPDRS-III and the H&Y scale. Statistical analyses were performed to assess associations between variables. Results: PDG scored lower on both olfactory tests and on the MoCA. Cognitive performance positively influenced olfactory scores, especially for SS-12 in both groups. Education significantly affected SS-12 and MoCA scores but had no important effect on mCCCRC performance. Age negatively impacted mCCCRC scores in the CG. Conclusion: although education significantly influenced SS-12 scores, our findings showed that mCCCRC performance was less affected by lower educational levels. This highlights the mCCCRC as a more education-independent olfactory test, suitable for use in populations with limited schooling. Integrating cognitive and olfactory testing may enhance clinical evaluation and monitoring in PD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".