Evaluation of the complete Sniffin Sticks Test versus its subtests in differentiating Parkinson's disease patients from healthy controls
Bibliographic record
Abstract
BACKGROUND: Hyposmia is one of the most common, as well as the first nonmotor condition in Parkinson disease (PD). The sniffin sticks test (SST) evaluates three different aspects of olfactory function: threshold (T), discrimination (D), and identification (I). The sum of the scores of these three subtests produce a global score of olfaction, the Threshold-Discrimination-Identification (TDI) score. OBJECTIVE: The aim of this study was to investigate if the TDI score or one of its subtests is better to discriminate PD patients from controls. METHODS: We recruited 27 PD patients and 17 healthy age-matched controls (HC) who were evaluated through a clinical interview, the Montreal Cognitive Assessment and Movement Disorders Society - Unified Parkinson Disease Rating Scale. The olfaction was assessed using the complete SST. RESULTS: < 0.001). The prevalence of olfaction impairment in our study (PD: 100%, and HC: 56%) was greater than that found in the literature. Cognition influenced the performance on TDI. The olfactory subtests were impaired proportionally between patients and controls. Furthermore, D and I were correlated, but only in PD patients. The TDI showed a tendency to better discriminate PD patients from HC, when compared with its subtests. CONCLUSIONS: Although the complete olfactory evaluation is time consuming, it seems to be a superior tool to identify olfaction impairment in PD patients, when compared with the isolated subtests.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".