Coronavirus disease 2019 related parosmia: an exploratory survey of demographics and treatment strategies
Bibliographic record
Abstract
OBJECTIVE: To investigate the clinical features, therapeutic efficacy and symptom time course of post-coronavirus disease 2019 parosmia. METHODS: A 22-item online questionnaire was distributed to AbScent research group and Facebook coronavirus disease 2019 anosmia group adult members to assess clinical features, interventions and their subjective efficacy for parosmia. RESULTS: A total of 209 participants (86 per cent females) reported: smell loss on average 3 days after coronavirus symptoms, recovery 4 weeks later, and first parosmia symptoms 12 weeks post infection. Respondents reported 10 per cent body weight loss, and listed onion and garlic as significant parosmia triggers. Regarding quality of life, depression was the most cited item (54 per cent). Smell training was trialled by 74 per cent of participants, followed by nasal corticosteroid spray (49 per cent). Stellate ganglion block, trialled by 16 per cent of respondents, had the highest reported improvement (45 per cent), with 21 per cent reporting a sustained benefit - the highest rate amongst registered treatment options. CONCLUSION: Post-coronavirus parosmia has a significant impact and remains challenging to treat. Stellate ganglion block appears to be successful relative to other reported treatments. Further research into the pathophysiology, efficacy and mechanism of stellate ganglion block effect is warranted.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".