Comparative Study of Chemosensory Dysfunction in COVID-19 in 2 Geographically Distinct Regions
Bibliographic record
Abstract
OBJECTIVE: To directly compare the prevalence of chemosensory dysfunction (smell and taste) in geographically distinct regions with the same questionnaires. METHODS: A cross-sectional study was performed to evaluate the self-reported symptoms among adults (older than 18 years) who underwent COVID-19 testing at an ambulatory assessment center in Canada and at a hospital in Israel between March 16, 2020, and August 19, 2020. The primary outcome was the prevalence of self-reported chemosensory dysfunction (anosmia/hypomsia and dysgeusia/ageusia). Subgroup analysis was performed to evaluate the prevalence of chemosensory deficits among the outpatients. RESULTS: We identified a total of 350 COVID-19-positive patients (138 Canadians and 212 Israelis). The overall prevalence of chemosensory dysfunction was 47.1%. There was a higher proportion of chemosensory deficits among Canadians compared to Israelis (66.7% vs 34.4%, P < .01). A subgroup analysis for outpatients (never hospitalized) still identified a higher prevalence of chemosensory dysfunction among Canadians compared to Israelis (68.2% vs 36.1%, P < 0.01). A majority of patients recovered their sense of smell after 4 weeks of symptom onset. CONCLUSION: Although the prevalence of chemosensory deficit in COVID-19 was found to be similar to previously published reports, the prevalence can vary significantly across different geographical regions. Therefore, it is important to obtain regionally specific data so that the symptom of anosmia/dysgeusia can be used as a guide for screening for the clinical diagnosis of COVID-19.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".