Validation of T-MoCA in the Screening of Mild Cognitive Impairment in Chinese Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background Atrial fibrillation (AF) is associated with a high risk of mild cognitive impairment (MCI) and dementia. However, feasible and simple instruments that facilitate the regular assessment of cognitive status in patients with AF remain underdeveloped. Methods Cognitive function was first evaluated using telephone Montreal cognitive assessment (T-MoCA), and then patients were invited for an in-person interview for cognitive assessment using both Clinical Dementia Rating (CDR) and mini-mental status evaluation (MMSE). Using CDR = 0.5 as a reference standard, the ability of T-MoCA and MMSE to discriminate cognitive dysfunction, stratified by education level, was tested by receiver–operating curve (ROC) analysis. The net reclassification index was calculated for comparison between the performance of T-MoCA and MMSE. Results One hundred and one patients completed both telephone and in-person interview. Thirty-five MCI patients were identified as MCI using the criteria of CDR = 0.5. The areas under the ROC curve of T-MoCA were 0.80 (0.71–0.89), 0.83 (0.71–0.95), and 0.85 (0.64–0.92) for all patients, patients with high educational level, and patients with low education level, respectively. The optimal threshold was achieved at 16/17 with a sensitivity of 85.7% and a specificity of 69.7% in overall patients, 15/16 with a sensitivity of 88.2% and a specificity of 64.5% in the low educational level patients, and 16/17 with a sensitivity of 77.8% and a specificity of 87.9% in the high educational level patients. Compared to the criterion MMSE ≤ 27 and MMSE norms for the elderly Chinese community, the stratified T-MoCA threshold improves correct classification by 23.7% ( p = 0.033) and 30.3% ( p = 0.020), respectively. Conclusion T-MoCA is a feasible and effective instrument for MCI screening in patients with AF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".