Comparison of the telephone‐Montreal Cognitive Assessment (T‐MoCA) and Telephone Interview for Cognitive Status (TICS) as screening tests for early Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Remote screening for cognitive impairment associated with Alzheimer's disease (AD) has grown in importance with the expected rise in prevalence of AD in an aging population and with new potential treatment options. METHODS: The Telephone Interview for Cognitive Status (TICS) and new telephone adaptation of the Montreal Cognitive Assessment (T-MoCA) were administered to participants independently classified through in-person clinical evaluation as cognitively normal (CN; n = 167), mild cognitive impairment (MCI; n = 25), or dementia (n = 23). Cerebrospinal fluid AD biomarkers were measured (n = 79). RESULTS: TICS and T-MoCA were highly correlated (r = 0.787; P < 0.001): groups differed on both (CN<MCI<dementia; TICS: F [2212] = 156.66; P < 0.001; T-MoCA: F [2210] = 143.72; P < 0.001), both effectively detected cognitive impairment (receiver operating characteristic area under the curve: TICS = 0.889; T-MoCA = 0.902), and both negatively correlated with a composite AD biomarker (tau/amyloid beta 1-42; TICS: r = -0.372; P = 0.001; T-MoCA: r = -0.480; P < 0.001). DISCUSSION: TICS and T-MoCA are effective for remotely detecting cognitive impairment associated with AD in older adults. Strong correlation between tests provides construct validity for the newer T-MoCA. HIGHLIGHTS: Construct validity for the telephone adaptation of the Montreal Cognitive Assessment (T-MoCA) was newly established against the Telephone Interview for Cognitive Status (TICS). TICS and T-MoCA effectively detected cognitive impairment with remote administration. Both tests negatively correlated with a composite cerebrospinal fluid Alzheimer's disease (AD) biomarker (tau/amyloid beta 1-42).
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".