Telephone Assessment of Cognition After Transient Ischemic Attack and Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Face-to-face cognitive testing is not always possible in large studies. Therefore, we assessed the telephone Montreal Cognitive Assessment (T-MoCA: MoCA items not requiring pencil and paper or visual stimulus) and the modified Telephone Interview of Cognitive Status (TICSm) against face-to-face cognitive tests in patients with transient ischemic attack (TIA) or stroke. METHODS: In a population-based study, consecutive community-dwelling patients underwent the MoCA and neuropsychological battery >1 year after TIA or stroke, followed by T-MoCA (22 points) and TICSm (39 points) at least 1 month later. Mild cognitive impairment (MCI) was diagnosed using modified Petersen criteria and the area under the receiver-operating characteristic curve (AUC) determined for T-MoCA and TICSm. RESULTS: Ninety-one nondemented subjects completed neuropsychological testing (mean±SD age, 72.9±11.6 years; 54 males; stroke 49%) and 73 had telephone follow-up. MoCA subtest scores for repetition, abstraction, and verbal fluency were significantly worse (P<0.02) by telephone than during face-to-face testing. Reliability of diagnosis for MCI (AUC) were T-MoCA of 0.75 (95% confidence interval [CI], 0.63-0.87) and TICSm of 0.79 (95% CI, 0.68-0.90) vs face-to-face MoCA of 0.85 (95% CI, 0.76-0.94). Optimal cutoffs were 18 to 19 for T-MoCA and 24 to 25 for TICSm. Reliability of diagnosis for MCI (AUC) was greater when only multi-domain impairment was considered (T-MoCA=0.85; 95% CI, 0.75-0.96 and TICSm=0.83, 95% CI, 0.70-0.96) vs face-to-face MoCA=0.87; 95% CI, 0.76-0.97). CONCLUSIONS: Both T-MoCA and TICSm are feasible and valid telephone tests of cognition after TIA and stroke but perform better in detecting multi-domain vs single-domain impairment. However, T-MoCA is limited in its ability to assess visuoexecutive and complex language tasks compared with face-to-face MoCA.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".