Can Multisensory Olfactory Training Improve Olfactory Dysfunction Caused by COVID-19?
Bibliographic record
Abstract
Approximately 30-60% of people suffer from olfactory dysfunction (OD) such as hyposmia or anosmia after being diagnosed with COVID-19; 15-20% of these cases last beyond resolution of the acute phase. Previous studies have shown that olfactory training can be beneficial for patients affected by OD caused by viral infections of the upper respiratory tract. The aim of the study is to evaluate whether a multisensory olfactory training involving simultaneously tasting and seeing congruent stimuli is more effective than the classical olfactory training. We recruited 68 participants with persistent OD for two months or more after COVID-19 infection; they were divided into three groups. One group received olfactory training which involved smelling four odorants (strawberry, cheese, coffee, lemon; classical olfactory training). The other group received the same olfactory stimuli but presented retronasally (i.e., as droplets on their tongue); while simultaneous and congruent gustatory (i.e., sweet, salty, bitter, sour) and visual (corresponding images) stimuli were presented (multisensory olfactory training). The third group received odorless propylene glycol in four bottles (control group). Training was carried out twice daily for 12 weeks. We assessed olfactory function and olfactory specific quality of life before and after the intervention. Both intervention groups showed a similar significant improvement of olfactory function, although there was no difference in the assessment of quality of life. Both multisensory and classical training can be beneficial for OD following a viral infection; however, only the classical olfactory training paradigm leads to an improvement that was significantly stronger than the control group.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".