The burden of prolonged smell and taste loss in covid-19
Bibliographic record
Abstract
It was sudden, like turning an electric switch off."This is how one patient described her abrupt loss of smell and taste following infection with covid-19."I was eating some lunch on day three after contracting covid-19 and one moment I could still smell and taste the flavours of my soup, and the next everything vanished."She was not alone in this.In fact, smell and taste loss are common complaints among patients with covid-19, with an estimated 50% of patients reporting these symptoms.1 This is thought to occur due to conductive barriers and nerve damage from the extensive inflammation in covid-19 infection.2 -5 The recovery of smell and taste is very much a gradual process for some.In our analysis of 3699 patients from 12 countries, recently published in The BMJ, we found that at the 30 day mark following the initial infection, only 74% and 79% of patients are expected to recover their smell and taste respectively.6 Recovery rates rise with each passing month, reaching a peak of 96% for smell and 98% for taste after six months.Using a mathematical technique known as cure modelling to project the plateaus of smell and taste recovery across 18 studies, we found that self-reported smell loss may persist in 5.6% of patients, while 4.4% may not recover their self-reported sense of taste (for some this reflects loss of flavour perception, a function of olfaction, while in others it may reflect true gustatory dysfunction).7 With more than 550 million confirmed covid-19 cases as of July 2022, this means that potentially 15 million and 12 million patients may experience long term smell and taste impairments, respectively, based on our modelling.This could contribute to the rising burden of long covid.Furthermore, self-reporting may over estimate recovery, 7 suggesting that the true burden of olfactory dysfunction is even greater.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".