Nostril Differences in the Olfactory Performance in Health and Disease
Bibliographic record
Abstract
In the past few decades, several olfactory tests have been developed to assess olfactory performance and detect disorders. Contrary to other sensory systems, both nostrils are usually tested together; we hypothesized that monorhinal testing may reveal side differences in sensitivity which may be useful for the diagnosis of olfactory dysfunction. Using the "Sniffin' Sticks" test, we assessed olfactory function of 458 participants (278 healthy controls, 180 hyposmic patients), one nostril after the other, with 3 different tasks. For each participant and each task, we compared the scores obtained with both nostrils, and defined the best and worst nostrils. Thus we were able to establish normative data and to define cut-off values. Our results suggest that scores obtained with the worst nostril are the most efficient in detecting an olfactory disorder. This supports the importance of monorhinal testing, as it can allow an earlier and more accurate diagnosis than birhinal testing. This may be especially useful in the context of early detection of neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".