Orthonasal, but not Retronasal Olfaction Is Specifically Impaired in Parkinson’s Disease
Bibliographic record
Abstract
Olfactory dysfunction (OD) in Parkinson's disease (PD) appears several years before the presence of motor disturbance. Olfactory testing has the potential to serve as a tool for early detection of PD, but OD is not specific to PD as it affects up to 20% of the general population. Olfaction includes an orthonasal and a retronasal components; in some forms of OD, retronasal olfactory function is preserved. We aimed to evaluate whether combined testing components allows for discriminating between PD-related OD and non-Parkinsonian OD (NPOD). The objective of this study is to orthonasal and retronasal olfactory function in PD patients and compare them to a NPOD group and to healthy controls. We hypothesized that this combined testing allows to distinguish PD patients from both other groups. We included 32 PD patients, 25 NPOD patients, and 15 healthy controls. Both olfactory components were impaired in PD and NPOD patients, compared with controls; however, NPOD patients had significantly better orthonasal scores than PD patients. Furthermore, the ratio of retronasal/orthonasal score was higher in PD than in both other groups. In the NPOD group, orthonasal and retronasal scores were significantly correlated; no such correlation could be observed in PD patients. In summary, PD patients seem to rely on compensatory mechanisms for flavor perception. Combined orthonasal and retronasal olfactory testing may contribute to differentiate PD patients from patients with NPOD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".