Position paper on olfactory dysfunction: 2023
Bibliographic record
Abstract
BACKGROUND: Since publication of the original Position Paper on Olfactory Dysfunction in 2017 (PPOD-17), the personal and societal burden of olfactory disorders has come sharply into focus through the lens of the COVID-19 pandemic. Clinicians, scientists and the public are now more aware of the importance of olfaction, and the impact of its dysfunction on quality of life, nutrition, social relationships and mental health. Accordingly, new basic, translational and clinical research has resulted in significant progress since the PPOD-17. In this updated document, we present and discuss currently available evidence for the diagnosis and management of olfactory dysfunction. Major updates to the current version include, amongst others: new recommendations on olfactory related terminology; new imaging recommendations; new sections on qualitative OD and COVID-19 OD; updated management section. Recommendations were agreed by all co-authors using a modified Delphi process. CONCLUSIONS: We have provided an overview of current evidence and expert-agreed recommendations for the definition, investigation, and management of OD. As for our original Position Paper, we hope that this updated document will encourage clinicians and researchers to adopt a common language, and in so doing, increase the methodological quality, consistency, and generalisability of work in this field.
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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 | 0.026 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.018 |
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".