Brain fog assessment in patients recovered from COVID-19 in China: a development and validation study
Bibliographic record
Abstract
Post coronavirus disease 2019 (COVID-19) pandemic, the widespread emergence and persistence of brain fog has led to a decline in people’s productivity and quality of life. However, the clinical characteristics of COVID-19-associated brain fog are unclear, and standardized assessments are lacking. This study aims to develop a scale for brain fog assessment and support clinical practice and research. The 17-item Brain Fog Assessment (BFA) scale was developed using a standardized methodology, including literature review, focus group discussions (FGDs), expert evaluation, and psychometric validation. Eighteen potential items were generated following the literature review. These items were subsequently refined during FGDs, which included input from patients, caregivers, and multidisciplinary experts in neurology, cognitive neuroscience, and psychology. After thorough deliberation and expert evaluation, the item pool was finalized into a 17-item scale. We recruited 1,325 patients recovered from COVID-19 from Chinese communities. Psychometric properties were assessed by reliability and validity analysis. Exploratory factor analysis of the BFA scale revealed a three-factor mode comprising ‘cognitive decline’ (nine items), ‘confusion − disorientation’ (five items), and ‘fatigue’ (three items). The internal consistency of each factor was strong (Cronbach’s α: 0.82–0.92). Confirmatory factor analysis showed that the model fit, convergent validity, and discriminant validity of the scale were satisfactory. The test-retest reliability was strong (intraclass correlation coefficient = 0.84). Criterion-related validity analysis showed a strong correlation to the Wood Mental Fatigue Inventory (r = 0.70, p < 0.001). Individuals with a higher BFA score tended to score lower on the Montreal Cognitive Assessment (r = −0.23, p = 0.015). We established a novel BFA scale to quantify multiple clinical aspects of COVID-19-associated brain fog. Using the BFA scale, fatigue and declining performance in memory, attention, and thought were identified as the main symptoms of COVID-19-associated brain fog. This scale has potential implications for disease monitoring and therapy development for individuals with COVID-19-associated brain fog.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".