Performance of a cost-effective olfactory test to evaluate hyposmia in Parkinson's disease patients
Bibliographic record
Abstract
Abstract Background Parkinson's disease (PD) causes motor and non-motor symptoms such as hyposmia, which is evaluated through olfactory tests in the clinical practice. Objective To assess the feasibility of using the modified Connecticut Chemosensory Clinical Research Center (mCCCRC) olfactory test and to compare its performance with the Sniffin' Sticks-12 (SS-12, Burghart Messtechnik GmbH, Wedel, Germany) test. Methods A transversal case-control study in which the patients were divided into the PD group (PDG) and the control group (CG). The cost and difficulty in handling substances to produce the mCCCRC test kits were evaluated. Sociodemographic characteristics, smoking habits, past coronavirus disease 2019 (COVID-19) infections, self-perception of odor sense, and cognition through the Montreal Cognitive Assessment (MoCA) were also evaluated. The PDG was scored by part III of the Unified Parkinson's Disease Rating Scale (UPDRS-III) and the Hoehn and Yahr Scale (H&Y) scale. Correlations were assessed through the Spearman rank correlation coefficient test (ρ, or rho). Results The mCCCRC test was easily manufactured and handled at a cost ten times lower compared with the SS-12. The groups (PDG: n = 34; CG: n = 38) were similar in terms of age, sex, level of schooling, smoking habits, and history of COVID-19. The tests results showed moderate correlation (rho = 0.65; p < 0.0001). The CG presented better cognitive performance and scored better in both tests (p < 0.0001). There was a tendency for a negative correlation with age, but good correlation with the MoCA (p = 0.0029). The results of the PDG group showed no correlation with olfactory results and motor performance or disease duration. The self-perception of hyposmia was low in both groups. Conclusion The mCCCRC is an easy-to-apply and inexpensive method that demonstrated a similar performance to that of the SS-12 in evaluating olfaction in PD patients and healthy controls.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".