Post-vaccination chemosensory outcomes in COVID-19-associated dysfunction
Bibliographic record
Abstract
ABSTRACT Olfactory and taste dysfunction are common symptoms of COVID-19 and post-acute COVID-19 syndrome, yet the effects of COVID-19 vaccines on chemosensory perception remain incompletely understood. This global, multilingual, online survey assessed post-vaccination changes in chemosensory function among individuals with and without COVID-19-related chemosensory impairment. Between May 2022 and August 2023, 2,955 responses were collected via convenience sampling, of which 1,352 were included in the analyses. Participants reported vaccination status, side effects, and chemosensory function before and after each vaccine dose. Pfizer-BioNTech accounted for 46.2% of doses, followed by Sputnik V (16.3%), Moderna (15.4%), AstraZeneca (8.9%), and Sinopharm (7.4%). More than 90% of participants reported no change in their general sense of smell or taste following vaccination, regardless of pre-existing chemosensory impairment. Among participants with qualitative chemosensory distortions (one-third of the sample), improvement was reported by 11-18% for parosmia, 20-29% for phantosmia, and 12-21% for taste distortion, depending on the vaccine dose, while worsening was reported by 3% or fewer. Side effects varied by vaccine type and were more frequent among individuals with worsened chemosensory symptoms. These findings suggest that COVID-19 vaccination is unlikely to adversely affect chemosensory function for most individuals. Given the observational design and reliance on self-reported data, the results should be interpreted cautiously. Future longitudinal studies using objective measures are needed to clarify these associations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".