Cognitive assessment in the time of pandemics: mandatory surgical face mask usage affects cognitive test performance of older adults
Bibliographic record
Abstract
BACKGROUND: The most important disadvantage of surgical mask usage is that it can aversely affect communication. This study aimed to evaluate the possible effects of face masks on the cognitive test performance of older adults. METHODS: A total of 198 geriatric patients were enrolled after applying the exclusion criteria. Within the comprehensive geriatric assessment (CGA), cognitive status assessment was performed with the Mini-Mental State Examination test (MMSE) and Quick Mild Cognitive Impairment Screening test (Q-MCI) tests. RESULTS: The median age was 70 (66-77) years, and there were 119 female (60.7%) patients. Patients were divided into normal cognitive status (NC), mild cognitive impairment (MCI), and probable Alzheimer's disease (AD) groups. There were 129 (65.2%), 30 (15.2%), and 37 (18.7%) patients in each group, respectively. For differentiating MCI from NC, calculated optimal cut-offs for the Q-MCI and MMSE total scores were ≤50 (sensitivity 83.3%, specificity 90.7%) and ≤26 (sensitivity 63.3%, specificity 87.5%), respectively. For differentiating AD from MCI, calculated optimal cut-offs for the Q-MCI and MMSE total scores were ≤28 (sensitivity 76.8%, specificity 86.7%), and ≤24 (sensitivity 94.4%, specificity 64.5%), respectively. CONCLUSION: Our results revealed that screening tests are still sensitive in discriminating cognitive disorders although cut-offs are lower with mask usage than for previously validated cut-offs. This is the first study revealing the impact of surgical mask usage on cognitive test performance, indicating that cut-offs validated before the pandemic may cause overdiagnosing of cognitive disorders since the previous cut-offs are not validated for mask usage. Large sample studies are needed to determine new cut-offs validated with mask usage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".