High prevalence of long-term olfactory disorders in healthcare workers after COVID-19: A case-control study
Bibliographic record
Abstract
BACKGROUND: More than a year after recovering from COVID-19, a large proportion of individuals, many of whom work in the healthcare sector, still report olfactory dysfunctions. However, olfactory dysfunction was common already before the COVID-19 pandemic, making it necessary to also consider the existing baseline prevalence of olfactory dysfunction. To establish the adjusted prevalence of COVID-19 related olfactory dysfunction, we assessed smell function in healthcare workers who had contracted COVID-19 during the first wave of the pandemic using psychophysical testing. METHODS: Participants were continuously tested for SARS-CoV-2 IgG antibodies since the beginning of the pandemic. To assess the baseline rate of olfactory dysfunction in the population and to control for the possibility of skewed recruitment of individuals with prior olfactory dysfunction, consistent SARS-CoV-2 IgG naïve individuals were tested as a control group. RESULTS: Fifteen months after contracting COVID-19, 37% of healthcare workers demonstrated a quantitative reduction in their sense of smell, compared to only 20% of the individuals in the control group. Fifty-one percent of COVID-19-recovered individuals reported qualitative symptoms, compared to only 5% in the control group. In a follow-up study 2.6 years after COVID-19 diagnosis, 24% of all tested recovered individuals still experienced parosmia. CONCLUSIONS: In summary, 65% of healthcare workers experienced parosmia/hyposmia 15 months after contracting COVID-19. When compared to a control group, the prevalence of olfactory dysfunction in the population increased by 41 percentage points. Parosmia symptoms were still lingering two-and-a half years later in 24% of SARS-CoV-2 infected individuals. Given the amount of time between infection and testing, it is possible that the olfactory problems may not be fully reversible in a plurality of individuals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".