Olfactory testing in children using objective tools: Comparison of Sniffin’ Sticks and University of Pennsylvania Smell Identification Test (UPSIT)
Bibliographic record
Abstract
BACKGROUND: Detection of olfactory dysfunction is important for fire and food safety. Clinical tests of olfaction have been developed for adults but their use in children has been limited because they were felt to be unreliable in children under six years of age. We therefore administered two olfactory tests to children and compared results across tests. METHODS: Two olfactory tests (Sniffin' Sticks and University of Pennsylvania Smell Identification Test (UPSIT)) were administered to 78 healthy children ages 3 to 12 years. Children were randomized to one of two groups: Group 1 performed the UPSIT first and Sniffin' Sticks second, and Group 2 performed Sniffin' Sticks first and UPSIT second. RESULTS: All children were able to complete both olfactory tests. Performance on both tests was similar for children 5 and 6 years of age. There was an age-dependent increase in score on both tests (p < .01). Children performed better on the Sniffin' Sticks than the UPSIT (65.3% versus 59.7%, p < .01). There was no difference in performance due to order of test presentation. CONCLUSIONS: The Sniffin' Sticks and UPSIT olfactory tests can both be completed by children as young as 5 years of age. Performance on both tests increased with increasing age. Better performance on the Sniffin' Sticks than the UPSIT may be due to a decreased number of test items, better ability to maintain attention, or decreased olfactory fatigue. The ability to reuse Sniffin' Sticks on multiple children may make it more practical for clinical use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".