Identification of Viruses in Patients With Postviral Olfactory Dysfunction
Bibliographic record
Abstract
OBJECTIVE: Causative viruses of postviral olfactory dysfunction (PVOD) have not yet been identified. The aim of this study was to investigate causative viruses in patients with PVOD. STUDY DESIGN AND METHODS: Nasal discharge was collected from 24 patients with PVOD. We investigated the presence of 10 viruses in nasal discharge and examined the time course, with regard to changes in olfactory dysfunction and nasal obstruction in patients with PVOD, using questionnaires, acoustic rhinometry, and olfactory tests. RESULTS: Rhinoviruses were detected in 10 patients by electrophoresis. Rhinoviruses were also confirmed in four patients by nucleotide sequences. Viral serotypes were identified to be human rhinovirus (HRV)-40, HRV-75, HRV-78, and HRV-80. One of the four patients complained of anosmia, whereas another complained of dysosmia. Olfactory testing did not show significant improvement at 4, 8, 11, and 24 weeks after the first visit in the four patients, although results of acoustic rhinometry significantly improved. Two of the four patients complained of olfactory dysfunction even 6 months after the first visit. Coronavirus and parainfluenza virus were detected in one patient each, and Epstein-Barr viruses were detected in three patients. CONCLUSIONS: This study for the first time detected rhinovirus, coronavirus, parainfluenza virus, and Epstein-Barr virus in nasal discharge of patients with PVOD. Furthermore, the present study suggests that rhinoviruses can cause olfactory dysfunction through mechanisms other than nasal obstruction and that rhinoviruses can induce various severities and different time courses of olfactory dysfunction.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".