Patterns of smell recovery in 751 patients affected by the COVID‐19 outbreak
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Post-viral olfactory dysfunction is well established and has been shown to be a key symptom of COVID-19 with more than 66% of European and US patients reporting some degree of loss of smell. Persistent olfactory dysfunction appears to be commonplace and will drive the demand for general practitioner, otolaryngology or neurology consultation in the next few months - evidence regarding recovery will be essential in counselling our patients. METHODS: This was a prospective survey-based data collection and telemedicine follow-up. RESULTS: In total, 751 patients completed the study, of whom 477 were females and 274 males. The mean age of the patients was 41 ± 13 years (range 18-60). There were 621 patients (83%) who subjectively reported a total loss of smell and 130 (17%) a partial loss. After a mean follow-up of 47 ± 7 days (range 30-71) from the first consultation, 277 (37%) patients still reported a persistent subjective loss of smell, 107 (14%) reported partial recovery and 367 (49%) reported complete recovery. The mean duration of the olfactory dysfunction was 10 ± 6 days (range 3-31) in those patients who completely recovered and 12 ± 8 days (range 7-35) in those patients who partially recovered. CONCLUSIONS: According to our results, at this relatively early point in the pandemic, subjective patterns of recovery of olfactory dysfunction in COVID-19 patients are valuable for our patients, for hypothesis generation and for treatment development.
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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 | 0.002 |
| 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 | 0.001 | 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".