Olfactory dysfunction as an early predictor for post‐COVID condition at 1‐year follow‐up
Bibliographic record
Abstract
Abstract Background Olfactory dysfunction together with neurological and cognitive symptoms are common after COVID‐19. We aimed to study whether performance on olfactory and neuropsychological tests following infection predict post‐COVID condition (PCC), persisting symptoms, and reduced health‐related quality of life. Methods Both hospitalized (N = 10) and non‐hospitalized individuals (N = 56) were enrolled in this prospective cohort study. Participants were evaluated 1–3 months after infection with an olfactory threshold test and neuropsychological tests, which was used as predictors of PCC. A questionnaire outlining persisting symptoms and the validated instrument EuroQol five‐dimension five‐level for health‐related quality of life assessment were used as outcome data 1 year after infection (N = 59). Principal component analysis was used to identify relevant predictors for PCC at 1 year. Results Objectively assessed olfactory dysfunction at 1–3 months post infection, but not subjective olfactory symptoms, predicted post‐COVID condition with reduced health‐related quality of life (PCC+) at 1 year. The PCC+ group scored more often below the cut off for mild cognitive impairment on the Montreal Cognitive Assessment (61.5% vs. 21.7%) and higher on the Multidimensional Fatigue Inventory‐20, compared to the group without PCC+. Conclusion Our results indicate that objectively assessed, olfactory dysfunction is a predictor for PCC+. These findings underscore the importance of objective olfactory testing. We propose that olfactory screening in the early post‐acute phase of COVID‐19 infection might identify individuals that are at higher risk of developing long‐term health sequalae.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".