Interrelatedness of Neurocognitive Domain Functioning Between Unprompted and Prompted Identification Testing With Psychophysical Olfactory Evaluation in a Post‐COVID‐19 Cohort
Bibliographic record
Abstract
ABSTRACT Objective Assessment of olfactory function with psychophysical testing requires cognitive demand to correctly pair test odors with remembered scents. Individuals suffering from long‐Corona Virus Disease 2019 (long‐COVID‐19) may develop a decline in neurocognitive performance, which may be concurrent with persistent olfactory dysfunction (OD). Given the rigorous cognitive demand of the unprompted identification (UI) olfactory assessment, the goal of this study is to understand whether it could serve as a proxy for specific neurocognitive domains during clinical assessment of olfaction. Methods Participants from our long‐COVID cohorts with persistent OD underwent a panel of neurocognitive screening followed by olfactory assessment of threshold followed by unprompted (UI) and prompted identification (PI) tests using Sniffin' Sticks. Hierarchical linear mixed‐effect models were used to understand the relative impact of each neurocognitive variable after controlling for demographics and olfactory threshold scores. Results Neurocognitive variables demonstrated common correlation trends. Models containing Montreal Cognitive Assessment (MoCA) and digit‐span backward scores had statistically significant fits for both UI (MoCA: χ 2 = 10.20, p = 0.001/digit‐span backward: χ 2 = 4.27, p = 0.04) and PI (MoCA: χ 2 = 4.51, p = 0.03/digit‐span backward: χ 2 = 5.04, p = 0.02) linear mixed‐effect models, but UI was further explained by logical memory ( χ 2 = 7.84, p = 0.005), verbal fluency ( χ 2 = 8.79, p = 0.003), and digit‐span forward ( χ 2 = 12.30, p = 0.0004). These relationships were statistically significant after controlling for demographic and olfactory threshold covariates. Conclusions UI and PI have interrelated neurocognitive dependence on global cognition (MoCA) and executive function (digit‐span backward) among long‐COVID participants. As UI draws upon neurocognitive domains of episodic (logical memory), semantic (verbal fluency), and working memory (digit‐span forward), the inclusion of a UI task may provide supplementary screening for cognitive impairments in those undergoing clinical olfactory assessment, particularly among those with lingering effects of COVID‐19.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".