Diagnostic accuracy of virtual cognitive assessment and testing: Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Virtual (i.e., telephone or videoconference) care was broadly implemented because of the COVID-19 pandemic. Our objectives were to compare the diagnostic accuracy of virtual to in-person cognitive assessments and tests and barriers to virtual cognitive assessment implementation. DESIGN: Systematic review and meta-analysis. SETTING: MEDLINE, EMBASE, CDSR, CENTRAL, PsycINFO, and gray literature (inception to April 1, 2020). PARTICIPANTS AND INTERVENTIONS: Studies describing the accuracy or reliability of virtual compared with in-person cognitive assessments (i.e., reference standard) for diagnosing dementia or mild cognitive impairment (MCI), identifying virtual cognitive test cutoffs suggestive of dementia or MCI, or describing correlations between virtual and in-person cognitive test scores in adults. MEASUREMENTS: Reviewer pairs independently conducted study screening, data abstraction, and risk of bias appraisal. RESULTS: Our systematic review included 121 studies (15,832 patients). Two studies demonstrated that virtual cognitive assessments could diagnose dementia with good reliability compared with in-person cognitive assessments: weighted kappa 0.51 (95% confidence interval [CI] 0.41-0.62) and 0.63 (95% CI 0.4-0.9), respectively. Videoconference-based cognitive assessments were 100% sensitive and specific for diagnosing dementia compared with in-person cognitive assessments in a third study. No studies compared telephone with in-person cognitive assessment accuracy. The Telephone Interview for Cognitive Status (TICS; maximum score 41) and modified TICS (maximum score 50) were the only virtual cognitive tests compared with in-person cognitive assessments in >2 studies with extractable data for meta-analysis. The optimal TICS cutoff suggestive of dementia ranged from 22 to 33, but it was 28 or 30 when testing was conducted in English (10 studies; 1673 patients). Optimal modified TICS cutoffs suggestive of MCI ranged from 28 to 31 (3 studies; 525 patients). Sensory impairment was the most often voiced condition affecting assessment. CONCLUSION: Although there is substantial evidence supporting virtual cognitive assessment and testing, we identified critical gaps in diagnostic certainty.
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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 | 0.038 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".