Presentation of New Onset Anosmia During the COVID-19 Pandemic
Bibliographic record
Abstract
INTRODUCTION: Anosmia has not been formally recognised as a symptom of COVID-19 infection. Growing anecdotal evidence suggests increasing incidence of cases of anosmia during the current pandemic, suggesting that COVID-19 may cause olfactory dysfunction. The objective was to characterise patients reporting new onset anosmia during the COVID-19 pandemic METHODOLOGY: Design: Survey of 2428 patients reporting new onset anosmia during the COVID-19 pandemic. SETTING: Volunteer sample of patients seeking medical advice of recent onset self-diagnosed loss of sense of smell RESULTS: 2428 surveys were completed within 7 days; 64% respondents were under 40. The majority of respondents reported onset of their anosmia in the last week. Of the cohort, 17% did not report any other symptom thought to be associated with COVID-19. In patients who reported other symptoms, 51% reported either cough or fever and therefore met current guidelines for self-isolation. CONCLUSIONS: Anosmia is reported in conjunction with well-reported symptoms of coronas virus, but 1 in 6 patients with recent onset anosmia report this as an isolated symptom. This might help identify otherwise asymptomatic carriers of disease and trigger targeted testing. Further study with COVID-19 testing is required to identify the proportion of patients in whom new onset anosmia can be attributed to COVID-19.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".