Olfactory recovery following infection with COVID-19: A systematic review
Bibliographic record
Abstract
Olfactory loss has been identified as one of the common symptoms related to COVID-19 infection. Although olfactory loss is recognized, our understanding of both the extent of loss and time to olfactory recovery following infection is less well known. Similarly, knowledge of potential impactful patient factors and therapies that influence olfactory recovery is desirable but is not overtly clear in the literature. Our systematic review sought to fill this knowledge gap. We included studies that: involved either an observational or an interventional design that reported data on patients with olfactory dysfunction due to Reverse Transcription Polymerase Chain Reaction (RT-PCR) diagnosed COVID-19 infection; and reported data regarding olfactory recovery measured by an objective olfactory test, Likert scale and/or visual analog scale (VAS). The study methods were determined a priori and registered in PROSPERO (Registration Number CRD42020204354). An information specialist searched Medline, Embase, LitCovid and the Cochrane Register of Controlled Trials up to March 2021, and two reviewers were involved in all aspects of study selection and data collection. After screening 2788 citations, a total of 44 studies of assorted observational designs were included. Patients had undergone objective COVID-19 testing, and most were adult patients with mild to moderate COVID-19. Olfactory recovery was found to occur as early as 7 days, with most patients recovering olfaction within 30 days. Few studies included prolonged follow-up to 6 months or longer duration. Poor olfaction at initial presentation was associated with poor recovery rates. Only a small number of studies assessed olfactory retraining and steroid therapy. Additional trials are underway.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".