Olfactory Training Impacts Olfactory Dysfunction Induced by COVID-19: A Pilot Study
Bibliographic record
Abstract
INTRODUCTION: Olfactory dysfunction is one of the main symptoms of COVID-19 and may last beyond resolution of the infection. The most promising intervention for post-viral olfactory dysfunction is olfactory training (OT), which involves exposing the olfactory system to a range of odors daily. This approach is thought of promoting the regeneration of olfactory receptor cells, but its effectiveness in patients with post-COVID-19 olfactory dysfunction has yet to be confirmed. METHODS: This double-blind randomized pilot study compared the effectiveness of OT versus placebo in the treatment of post-COVID-19 olfactory dysfunction. Twenty-five participants were recruited in each group. OT protocol consisted of sniffing 4 scents (rose, orange, clove, and eucalyptus) for 5 min twice daily for 12 weeks. Olfactory function was assessed before and after the training using (1) a validated odor identification test (UPSIT-40) and (2) a 10-point visual analog scale; we further assessed the presence of (3) parosmia. RESULTS: While we did not observe any effect of OT on olfactory test scores, we observed a significant improvement of subjective olfactory function in the intervention group, while no such effect was observed in the placebo group. Finally, the frequency of parosmia was significantly lower in the intervention group. CONCLUSIONS: This study highlights an increase in subjective but not objective olfactory function when performing OT for 12 weeks. Further, parosmia seems to be positively affected by OT. These results may serve as a starting point for larger scale studies to assess the efficacy of OT for treatment of post-COVID-19 olfactory dysfunction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".