Validation of the Telephone Interview of Cognitive Status and Telephone Montreal Cognitive Assessment Against Detailed Cognitive Testing and Clinical Diagnosis of Mild Cognitive Impairment After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Assessment of cognitive status poststroke is recommended by guidelines but follow-up can often not be done in person. The Telephone Interview of Cognitive Status (TICS) and the Telephone Montreal Cognitive Assessment (T-MoCA) are considered useful screening instruments. Yet, evidence to define optimal cut-offs for mild cognitive impairment (MCI) after stroke is limited. METHODS: We studied 105 patients enrolled in the prospective DEDEMAS study (Determinants of Dementia After Stroke; NCT01334749). Follow-up visits at 6, 12, 36, and 60 months included comprehensive neuropsychological testing and the Clinical Dementia Rating scale, both of which served as reference standards. The original TICS and T-MoCA were obtained in 2 separate telephone interviews each separated from the personal visits by 1 week (1 before and 1 after the visit) with the order of interviews (TICS versus T-MoCA) alternating between subjects. Area under the receiver-operating characteristic curves was determined. RESULTS: Ninety-six patients completed both the face-to-face visits and the 2 interviews. Area under the receiver-operating characteristic curves ranged between 0.76 and 0.83 for TICS and between 0.73 and 0.94 for T-MoCA depending on MCI definition. For multidomain MCI defined by multiple-tests definition derived from comprehensive neuropsychological testing optimal sensitivities and specificities were achieved at cut-offs <36 (TICS) and <18 (T-MoCA). Validity was lower using single-test definition, and cut-offs were higher compared with multiple-test definitions. Using Clinical Dementia Rating as the reference, optimal cut-offs for MCI were <36 (TICS) and approximately 19 (T-MoCA). CONCLUSIONS: Both the TICS and T-MoCA are valid screening tools poststroke, particularly for multidomain MCI using multiple-test definition.
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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 | 0.023 | 0.059 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".