Cognitive deficits and cortical volume loss in <scp>COVID</scp>‐19‐related hyposmia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Studies have found that up to 73% of COVID-19 patients experience hyposmia. It is unclear if the loss of smell in COVID-19 is due to damage to the peripheral or central mechanisms. This study aimed to explore the impacts of COVID-19-induced hyposmia on brain structure and cognitive functions. METHODS: The study included 36 hyposmic (h-COV) and 21 normosmic (n-COV) participants who had recovered from mild COVID-19 infection, as well as 25 healthy controls (HCs). All participants underwent neurological examination, neuropsychiatric assessment and Sniffin' Sticks tests. High-resolution anatomical images were collected; olfactory bulb (OB) volume and cortical thickness were measured. RESULTS: Addenbrooke's Cognitive Examination-Revised total and language sub-scores were slightly but significantly lower in the h-COV group compared to the HC group (p = 0.04 and p = 0.037). The h-COV group exhibited poorer performance in the Sniffin' Sticks test terms of discrimination score, identification score and the composite score compared to the n-COV and HC groups (p < 0.001, p = 0.001 and p = 0.002 respectively). A decrease in left and right OB volumes was observed in the h-COV group compared to the n-COV and HC groups (p = 0.003 and p = 0.006 respectively). The cortical thickness analysis revealed atrophy in the left lateral orbitofrontal cortex in the h-COV group compared to HCs. A significant low positive correlation of varying degrees was detected between discrimination and identification scores and both OB and left orbital sulci. CONCLUSION: Temporary or permanent hyposmia after COVID-19 infection leads to atrophy in the OB and olfactory-related cortical structures and subtle cognitive problems in the long term.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".