Accuracy of Telephone-Based Cognitive Screening Tests: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Telephone-based cognitive assessments may be preferable to in-person testing in terms of test burden, economic and opportunity cost. OBJECTIVE: We sought to determine the accuracy of telephone-based screening for the identification of dementia or Mild Cognitive Impairment (MCI). METHODS: Five multidisciplinary databases were searched. Two researchers independently screened articles and extracted data. Eligible studies compared any multi-domain telephone-based assessment of cognition to the face-to-face diagnostic evaluation. Where data allowed, we pooled test accuracy metrics using the bivariate approach. RESULTS: From 11,732 titles, 34 papers were included, describing 15 different tests. There was variation in test scoring and quality of included studies. Pooled analyses of accuracy for dementia: Telephone Interview for Cognitive Status (TICS) (<31/41) sensitivity: 0.92, specificity: 0.66 (6 studies); TICSmodified (<28/50) sensitivity: 0.91, specificity: 0.91 (3 studies). For MCI: TICS-modified (<33/50) sensitivity: 0.82, specificity: 0.87 (3 studies); Telephone-Montreal Cognitive Assessment (<18/22) sensitivity: 0.98, specificity: 0.69 (2 studies). CONCLUSION: There is limited diagnostic accuracy evidence for the many telephonic cognitive screens that exist. The TICS and TICS-m have the greatest supporting evidence; their test accuracy profiles make them suitable as initial cognitive screens where face to face assessment is not possible.
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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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".